Schedule

05 September

06 September

05 September

06 September

Speakers

Sponsors

Job Board

Exhibitors

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Aaron Roth

Associate Professor
University of Pennsylvania
Aaron Roth is the class of 1940 Bicentennial Term associate professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and research awards from Google, Amazon, and Yahoo. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, and machine learning. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.” Together with Michael Kearns, he is the author of “The Ethical Algorithm”, forthcoming in 2019 by Oxford University Press.

05 September

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Adam Stewart

Director, Sales Innovation Technology
Introhive
Adam has over 20 years’ experience helping professional services firms & Fortune 500 companies be more productive while generating more revenue. Adam currently leads sales innovation technology efforts at Introhive. Introhive's Relationship Data Science platform for CRM drives user adoption and increases your firm's marketing and business development efficiency. Data automation increases productivity and improves accuracy and completeness of data in CRM. AI-powered relationship intelligence helps the firm understand “who knows who” between partners, staff and the outside world. Relationship managers get the data science behind key contacts to drive revenue, cross sell new practice areas, and increase client retention.

06 September

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Alan King

Research Staff Member
IBM TJ Watson Research Center
Alan King is an applied mathematician with IBM's Thomas J. Watson Research Center in Yorktown Heights, New York. His current focus is on AI and Blockchain applications for the Financial Services Sector.

05 September

05 September

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Alexander Young

EVP, Insurance
Optimity
Alex Young has made a career in the insurance industry as both an industry insider and as a provider to the industry of new technologies. His carrier experience includes 10 years with Sun Life of Canada as a Chief Financial Officer where he was responsible for Policy Administration, Customer Service, Reinsurance, and Distribution Support. As a provider of new technologies, Alex has managed the Americas for a number of companies entering the Americas market including Fineos, eBaotech, and Tower Technologies (Now Opentext). As a frequent speaker he has covered topics such as predictive modeling in underwriting risk, using new data for fraud detection in underwriting and claim, agency distribution challenges, and using new technology for insurance operations. He has also been ranked in the top 1/10th of 1% in PUBG and came in second in a NASCAR race (To be fair, there were 2 cars).

06 September

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Amir Sepasi

Director, Advanced Analytics
Manulife
Amir Sepasi is the Lead Data Scientist for Group Advanced Analytics at Manulife Financial. He is currently leading the Global Fraud Analytics Center of Expertise (CoE), working on innovative ways to track the ever-evolving capabilities of fraudsters. His team has recently developed an application entitled Advanced AML Alert (AAA) which uncovers hidden, very sophisticated money laundering patterns using graph databases and link analysis. Amir received a 2018 Star of Excellence, the most prestigious employee recognition program in Manulife.
Prior to his current role at Manulife Financial, Amir has worked at several organizations including IBM and Scotiabank in the field of data science. He has a PhD in electrical and computer engineering.

Key Takeaways: 1)Importance of connected data for Advanced Analytics 2)Benefits of Graph Database 3)Importance of Network Algorithms

05 September

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Anna Arakelyan

Lead Data Scientist, Assistant Vice President
Mass Mutual
Anna Arakelyan is a Lead Data Scientist in the Sales and Marketing domain, Assistant Vice President at MassMutual. She holds a PhD in Economics from CUNY Graduate Center and an M.S. and B.S. in Quantitative Economics from Lomonosov Moscow State University. In her doctoral studies she focused on applied econometric analysis of social networks. Her areas of expertise are econometrics, statistics and probability, machine learning, causal inference, and design of experiments.

05 September

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Anurag Setty

Lead Data Scientist
Barclays US
Anurag Setty serves as Lead Data Science Consultant at Barclays US where he is responsible for developing new capabilities in Machine Learning, Deep Learning and Artificial Intelligence. He leads applied research initiatives in data science modeling, tooling and decision engines. Anurag has 5+ years of experience as data scientist in technology and financial institutions. He has a bachelor’s degree in Engineering Physics from Indian Institute of Technology (IIT) Bombay, a Master of Science and PhD in Physics from University of Maryland where he worked on time series forecasting techniques in highly complex systems.

05 September

05 September

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Artit Wangperawong

Distinguished Engineer, Artificial Intelligence
U.S. Bank
Artit ‘Art’ Wangperawong is a Distinguished Engineer at U.S. Bank applying ML and AI to solve problems across the organization. Art leads machine learning and artificial intelligence projects across corporate and retail banking. In this role, he identifies and evaluates business opportunities with strategic product potential, market and technology trends, key providers, legal/regulatory climate, and profitability. Art also conducts research and development, representing U.S. Bank to internal stakeholders, customers and partners.
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Arturo Gonzalez

Quantitative Analyst R&D
Bancolombia
Arturo Gonzalez is a Quantitative Analyst R&D at Bancolombia, one of the largest banks in Colombia. Arturo focuses on econometric analysis applied in finance and economic topics. His recent work has been in the area of nowcasting macroeconomic variables for the Colombian economy and selected economies in Central America. Arturo Holds a MSc in Finance from Externado University as well as a Bachelor of Economics at National University of Colombia and Control Engineering from FJC District University. Before joining Bancolombia, Arturo worked as analytics project manager at FNA financial institution where he focused in creating a churn models and predictive models for household loan disbursement.

06 September

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Baiju Devani

SVP, Chief Data Officer
AVIVA
Baiju has led data sciences and engineering teams for over a decade to bring algorithmic and data-driven business growth in FinTech and InsureTech space. As SVP and Chief Data Officer at Aviva Canada, Baiju is responsible for embedding the use of data and algorithms driving business growth. He leads a cross-functional team of actuaries, data scientists and data engineers to deliver innovative data-driven solutions including use of applied machine-learning for product pricing and underwriting and other advanced algorithms for decision making. Prior to Aviva, Baiju led the analytics group at IIROC where he guided decision making and algorithmic surveillance in trading markets that generate up to 600 million real-time data points daily. Baiju was also at OANDA, a FinTech startup that disrupted retail foreign-exchange markets where he led data sciences and business growth and was part of a team that scaled the organization from a startup to a 400+ employee global business. Baiju has a BSc and MSc in Computer Science from Queen's university and developed his data chops working on large biological datasets as part of his graduate work and later at the Ontario Cancer Institute.

05 September

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Bernard Goyder

Reporter
The Insurance Insider
Bernard is a senior reporter at The Insurance Insider in New York, where he specializes in InsurTech. He joined the publication in 2017 from Dow Jones, and was based in London before moving to the US earlier this year.

06 September

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Bill Lin

Senior Data Scientist
Nasdaq
Xuyang(Bill) Lin is a Senior Data Scientist at NASDAQ’s Machine Intelligence Lab, a group dedicated to leveraging AI to improve financial markets and solutions. His previous projects covered financial derivatives, alternative data research, portfolio construction, and model surveillance. He holds a Masters of Finance degree from MIT.

06 September

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Bindu Reddy

CEO & Co-Founder
RealityEngines.AI
Bindu Reddy is the CEO and Co-Founder of RealityEngines.AI. RealityEngines.AI is a foundational AI research company that solves the hard problems that enterprises face in the AI/ML space, and packages that research into easy to use, pay as you cloud services. Before starting RealityEngines.AI, she was the General Manager for AI Verticals at AWS, AI. She started the AI verticals organization at AWS, which created and launched Amazon Personalize and Amazon Forecast, the first of their kind AI services that enable organizations to create custom deep-learning models easily. Prior to that, she was the CEO and co-founder of Post Intelligence, a deep-learning company that created services for social media influencers that was acquired by Uber. Bindu is proud to be a Xoogler (ex-Googler) where she was the Head of Product for Google Apps, including Docs, Spreadsheets, Slides, Sites and Blogger. Bindu has a Masters Degree from Dartmouth and B.Tech degree from Indian Institute of Technology, Mumbai.
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Boyi Xie

Head Modelling & Insights Americas, Vice President
Swiss Re
Boyi Xie is the Head of Modelling & Insights Americas at Swiss Re. His work focuses on developing AI-backed insurance solutions for different lines of business, including Life & Health, and Property & Casualty insurance. His work has helped digital transformation and the adoption of AI/machine learning in insurance products and operations. Boyi obtained his Bachelor's degree from Zhejiang University and Ph.D. from Columbia University.

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Carlos Ignacio Patiño-Florez

Analytics Capabilities Manager
Bancolombia
Carlos Patino currently works at the largest Colombian retail bank, Bancolombia, as Analytic Capabilities Manager. This team operates transversally to all operational and strategic areas of the bank, and collaborates with existing analytics and data science teams in the adoption of Big Data technologies, and Machine Learning techniques, aimed at solving business problems, and at the creation of data and ML-based products that benefit the bank’s 9 million clients. In his role, Carlos develops ML models using Spark, Python and other tools such as CDH. Prior to joining Bancolombia, Carlos spent three years working as an Advanced Modeler for the Customer Strategy Management group at PNC Bank in Pittsburgh, PA. Carlos holds a Masters in Public Policy and Management from Carnegie Mellon University, and a Bachelor in Economics from ICESI University in Cali, Colombia.

06 September

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Christopher Johannessen

Director Digital Services and Data Science
Sia Partners
Chris Johannessen is an analytics, workplace automation, digital transformation and marketing technology pioneer, who has contributed innovations and leadership and volunteer service to the broader industry and academia over the course of his career. He has over 20 years of experience in analytics, digital strategy, and process automation / digitization.

Chris current serves Sia Partners as Director of Digital Services and Data Science. His recent area of focus is Process Optimization and Automation - a future-forward approach to modernizing organizations, blending an array of process improvement methods such as Lean, Design Thinking, Six Sigma and Agile, paired with a technology framework called "Smarter Workplace Automation" (featuring technologies and techniques such as Data Science, AI, Robotic Process Automation (RPA), Smart Low Code Workflow, Blockchain and Cognitive Interaction).

Chris has led, advised and served as the principal subject matter expert for teams across every industry and government vertical as a CMO, Vice President, product manager, team leader and emerging technology researcher across the arc of his career. He holds 2 US Patents (with a 3rd application pending), and is the recipient of several awards throughout his enterprise career - and for his work with non-profits and academia.

Prior to joining Sia, Chris served in leadership roles at organizations such as McKinsey, Omnicom, Barclays, eBay and General Electric. Chris is currently on the Editorial Board of The Journal of Marketing Analytics, as well as the Industry Advisory Board for Temple University’s Global Center for Big Data and Mobile Analytics. In the past, he has served on the advisory board of The Wharton School’s Customer Analytics Initiative and other universities / industry organizations.

05 September

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Courtney McCormac

Director, Enterprise Data & Integration Analysis
New York Life
Courtney McCormac currently works at New York Life Insurance Company leading the Enterprise Data and Integration Analysis function. She has spent over twenty years in FinTech dedicated to technology projects from trading systems development partnerships to now working with the New York Life Center for Data Science and Analytics to deploy models at scale. Courtney holds a degree from Boston College in Finance and Operations & Strategic Management. She leads the New York Life Business Analyst Network as well as the Women in Tech initiative and is a proud mom of two.

06 September

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David Jaw

Director of Data Science
Trupanion
Dave created and currently leads the data science function at Trupanion. His first interaction with machine learning was during his graduate research 10 years ago, where he was tasked with extracting a control signal for moving robots using only human thoughts. He has been convinced that ml will change the world ever since. He is currently working through the challenge of integrating data science into an organization in a way that maximizes long term mutual benefit, while being the best leader possible for his team.

06 September

06 September

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Deborah Barta

Senior Vice President, Innovation & Startup Engagement
Mastercard
Deborah Barta is the Senior Vice President of Innovation and Startup Engagement at Mastercard. Ms. Barta leads global innovation initiatives to experiment with new technologies, accelerate new concepts to market, and deliver transformative solutions to drive material growth. She also leads Mastercard's engagement with the global startup community through Start Path, the company's award-winning program that sources the world's leading startups as collaborators to build the future of commerce together.

06 September

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Devin Devrai

Associate
American Family Ventures
Devin is an associate at American Family Ventures focused on sourcing and evaluating new investment opportunities as well as supporting the firm’s portfolio companies. Prior to joining AFV, Devin worked in investment banking at Credit Suisse in New York City with the Leveraged Finance Origination and Restructuring Group. There he assisted in the evaluation and structuring of high yield and leveraged loan issuances for LBOs, acquisition financings, refinancings, and restructurings across the TMT, healthcare, and consumer retail verticals. He holds a BS from Duke University.

06 September

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Emma Maconick

Partner
Shearman & Sterling LLP
Emma Maconick is a partner in the Intellectual Property Transactions Group. She focuses on intellectual property, data protection, privacy and security issues for major technology clients engaged in data and innovation intensive activities. Emma represents corporate clients as well as lenders, emerging companies, research and development entities and universities. Emma focuses on representing clients in a range of technology sectors including FinTech, big data and analytics, cloud and edge computing, “as a service” businesses, consumer platform operators, artificial intelligence, machine learning, advanced virtual and augmented reality, autonomous mobility, and semi-conductor, hardware and software product designers and manufacturers. Emma has extensive experience with the IP, data and IT aspects of transactional matters including mergers & acquisitions, strategic alliances, joint ventures, capital markets transactions and corporate and financial investment. Emma also consults on IP litigation, with a focus on settlement agreements, coexistence agreements, cross-licenses and other arrangements for sharing IP rights in the technology content and telecommunications sectors. Emma frequently speaks and writes on cutting-edge issues in data protection, intellectual property and technology.

05 September

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Emmanuel Vallod

CEO
SumUp Analytics
Emmanuel is CEO and co-founder of SumUp Analytics, an AI startup based in San Francisco developing an ultra-fast, large-scale text analytics platform. Prior to co-founding SumUp Analytics, Emmanuel served as Head of Research for Mortgages and Securitized Credit at BlackRock and was a member of the Systematic Fixed Income investment committee. While at BlackRock, he oversaw investment solutions ranging from multi-strategy hedge-funds to smart beta products, ETFs and long-only institutional solutions, totaling ~$65 billion of AUM. He also co-invented BlackRock first smart-beta fixed-income solution: FIBR. Emmanuel was a proprietary trader at Societe Generale in New York before BlackRock and started his career in finance at Caisse des Depots et Consignations. Emmanuel holds an MFE from UC Berkeley and an MSc in Applied Math from Ecole Centrale Lyon.

06 September

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Frédéric Godin

Assistant Professor
Concordia University
Frédéric Godin is an Assistant Professor at Concordia University (Montreal, Canada) in the Mathematics and Statistics Department. His expertise and areas of research are financial engineering, risk management, actuarial science and data science. He also holds the Fellow of the Society of Actuaries (FSA) and Associate of the Canadian Institute of Actuaries (ACIA) designations. He published several papers in various international mathematical finance and actuarial science journals such as Quantitative Finance, Journal of Risk and Insurance, ASTIN Bulletin, Scandinavian Actuarial Research, Insurance: Mathematics and Economics and Journal of Economic Dynamics and Control. His most active research topics are dynamic hedging procedures, variable annuities and derivatives pricing.

05 September

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Gene Beidl

Data Scientist
FINRA
Gene Beidl is a Wall Street Quantitative Analyst turned Data Scientist.After years on Wall Street at institutions such as Salomon Brothers and Barclays Capital, Specializing in Fixed Income and Derivatives, he has worked in both New York and London both as a “quant” and as a structured products specialist in Emerging Markets.

Gene left Wall Street to pursue independent consulting roles and turned to applying data science and machine learning in finance and other industries. He currently works at the Financial Industry Regulatory Authority (FINRA) applying these techniques to detecting market manipulations in equities, ETFs and derivatives.

Gene holds engineering and computer science Bachelor’s and Master’s degrees from the Massachusetts Institute of Technology (MIT), with a concentration in Financial Engineering.

05 September

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George Lee

Director of Artificial Intelligence
Travelers
George Lee is a Director of Artificial Intelligence working at Travelers Insurance. He is an Artificial Intelligence Leader and Inventor with multiple patents pending. George has 15 years software development experience in the insurance and entertainment industry. Since 2015, George has worked on machine learning and deep learning models. He has extensive experiences in enterprise Architecture, Data Science, Computer Vision, Natural Language Processing (NLP), Blockchain, and mobile development.

06 September

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Hanoz Bhathena

Data Scientist - Machine Learning
UBS
Hanoz Bhathena is a Data Scientist within the Evidence Lab Innovations division at UBS, where he is responsible for developing machine learning models that uncover insights relevant to investment research. He has experience executing and leading AI/ML projects, particularly those that utilize deep learning, with applications focused on natural language understanding, recommendation systems and search. He holds a Master’s degree in Operations Research from Columbia University and a Bachelor’s degree in Electrical Engineering from the University of Mumbai, VJTI. He has also completed the Artificial Intelligence Graduate Certificate program from Stanford University.

06 September

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Igor Halperin

Professor of Financial Machine Learning/AI Asset Management
NYU/Fidelity Investments
Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining NYU Tandon, Igor was an Executive Director of Quantitative Research at JPMorgan, and before that he worked as a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has also co-authored the book “Credit Risk Frontiers” published by Bloomberg LP. Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. He advices a several fintech and data science start-ups and risk management firms.

05 September

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James Brusseau

Director of Data Ethics Site
Pace University
James Brusseau (PhD, Philosophy) is author of books, articles, and digital media in the history of philosophy and ethics. He has taught in Europe, Mexico, and currently at Pace University near his home in New York City. As Director of Data Ethics Site, a research institute currently incubating at Pace University, he explores the human experience of big data technologies.

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Jameson Tucker Allen

Data Engineer
Chubb
Born-again Data Science convert. After six years of designing skyscrapers for top-10 engineering firms, I decided to finally align my career with my interests. I quit my comfortable engineering job to engage in a full-time Data Science Immersive course at General Assembly in NYC, and began teaching Data Science immediately upon graduation. Along the way, I've developed dozens of data science passion projects ranging from predicting West Nile Virus outbreaks to analyzing the deteriorating state of the traditional U.S. higher education system. I've been building bespoke ETL pipelines and solving data problems at Chubb since February, 2019.

05 September

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James Sherer

Partner
Baker & Hostetler LLP
James Sherer is a Partner in the New York office of BakerHostetler, where he chairs the Information Governance and Artificial Intelligence practice teams and serves as part of the eDiscovery and Management and Privacy and Data Protection groups. James’s work and scholarship focuses on litigation; discovery management processes; enterprise risk; records and information governance; data privacy, security, and bank secrecy; technology integration issues; artificial intelligence; social media and the Internet of Things, and related merger and acquisition diligence. James holds an MBA, the CIPP/US, CIPP/E, CIPM, FIP, and PLS data privacy professional credentials, the CIP and IGP information governance designations, the UCLA Extension Global Cyber Institute’s Cybersecurity Certification, and the CEDS and eDPC eDiscovery specialist credentials.

05 September

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Jas Maggu

Founder & CEO
Galaxy.ai
Jas is the CEO and founder of Galaxy.AI (www.galaxy.ai). She has been part of the boards of early-stage companies and advised companies on business strategy and development. She has been a founder of a start-up previously and has also worked with several technology companies as part of a Venture Capital and Private Equity funds across various industry verticals. She has worked in three other countries apart from the US – UK, Canada, and India. Jas has also received a Commonwealth Scholarship offered by the University of Cambridge from where she graduated with her Masters and also holds Bachelors in Engineering from Panjab University.

06 September

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Jean-Marc Levy

CEO
ComplySci
Jean-Marc drives ComplySci’s strategy and growth, new product development and innovation, and expansion into new markets. He’s passionate about building technology solutions that can transform ineffective practices and empower professionals to become better strategic partners within their businesses.

05 September

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Joan Gelpi

SVP, Head of Data Science
AIG
Joan Gelpi has over 15 years of experience building and leading data science organizations in Fortune 100 companies. He has spent most of his career in Financial Services and other highly regulated industries such as Healthcare. He currently leads the Data Science organization at AIG. Prior to AIG he built and led a Data Science organization in American Express. He holds a PhD in Operations Research from Univeristat Politecnica Catalunya and an MBA from University of Chicago.
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Josh Cohen

Principal
Liberty Mutual Strategic Ventures
Josh Cohen is a principal at Liberty Mutual Strategic Ventures, the corporate venture capital arm of Liberty Mutual Insurance. Josh looks to invest in early stage hardware, software, platform, and services companies who are reshaping the insurance landscape and creating value for policyholders. His investment focus areas include the future of mobility, shelter, and small commercial.

06 September

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Julia Romero

Lead for Actuarial Engineering and Advanced Modeling
Haven Life
Julia Romero is the lead for Actuarial Engineering and Advanced Modeling at Haven Life, an online life insurance agency that’s backed and wholly owned by MassMutual. At Haven Life, Julia is focused on integrating and applying data science and other analytics models to drive innovation in actuarial technology. Prior to joining Haven Life, Julia worked as an actuary at AXA US, where she focused on the development of agent based models of annuity policyholder behavior.

05 September

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Kamalesh Rao

Senior Data Scientist
Société Générale
Kamalesh Rao is a Senior Data Scientist at Societe Generale, where he leads a Data Science team that aims to bring AI/ML practices to all parts of the bank, Prior to joining SG, he worked as a Director of Economic Research at MasterCard, helping pioneer the company’s efforts to bring alternative/big data to the markets. He has appeared on Bloomberg News, CNBC and been interviewed by NPR, NYT and BusinessWeek.

06 September

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Kaushik Pamulaparthy

Senior Data Scientist
Northwestern Mutual

Kaushik Pamulaparthy is a senior data scientist at Northwestern Mutual, working on projects involving Machine Learning to enhance the financial planning experience for clients and financial advisors alike, as part of the organization’s digital transformation. He is passionate about the responsible use of AI. Previously, he worked on patient health analytics as a data scientist at AbleTo - a behavioral telehealth startup whose clients include health insurers, and began his career advising insurance and financial institutions as a decision analytics consultant at EXL. He holds a Masters in Operations Research from Columbia University, and is an alumnus of International House NYC. 

06 September

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Kishore Karra

Vice President, Model Risk Governance & Review
J.P. Morgan
Kishore Karra is the lead reviewer of models used in Anti-Money Laundering at JP Morgan Chase. In this role, he assesses and mitigates risk posed by models used for the purposes of Sanctions Screening and Transaction Monitoring. Kishore holds a Master's degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.

06 September

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Lakshmi Manohar Akella

Data Science & AI Director
Anthem

Lakshmi Manohar Akella is a Data Science/AI Director in the Enterprise Data Science team with Anthem. He has over 14 years of experience in conducting applied research and developing Machine Learning approaches for a variety of problems in e-commerce, health care and pharmaceutical industries. He leads ML projects end to end from conception of an algorithmic idea to production of real-time machine learning systems. Currently he works on Deep Learning methods for clinical event prediction and disease progression modeling.

05 September

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Leman Akoglu

Associate Professor
Carnegie Mellon University
Leman Akoglu joined the Heinz College faculty as an Assistant Professor in Fall 2016. She also holds a courtesy appointment in the Computer Science Department (CSD) and the Machine Learning Department (MLD) of School of Computer Science (SCS). Prior to this she was an Assistant Professor in the Department of Computer Science at Stony Brook University since receiving her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012.

05 September

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Marsal Gavalda

Head of Machine Learning
Square
Marsal Gavalda is a senior R&D executive with deep expertise in speech, language, and machine learning technologies. Marsal currently heads the Commerce Platform Machine Learning team at Square, where he applies machine learning and automation for Square's overarching purpose of economic empowerment. Marsal holds a PhD in Language Technologies and a MS in Computational Linguistics, both from Carnegie Mellon University, and a BS in Computer Science from BarcelonaTech. Marsal is the author of over thirty technical and literary publications, thirteen issued patents, and is fluent in six languages.

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Michael Natusch

Global Head of AI
Prudential
Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential last year from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a ‘Silicon Roundabout’ based startup and prior to that was a partner at a major consulting firm. Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society.

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Min Yu

Senior Data Scientist
AXIS Capital
Min Yu is a Senior Data Scientist at Axis Capital. She specializes in AI transformation of the insurance business including personal, commercial, and specialty lines by developing and deploying machine learning models within the business process. Min holds a Ph.D. in Physics from the University of Illinois at Urbana-Champaign.

05 September

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Natalia Bailey

Associate Policy Advisor, Digital Finance
Institute of International Finance
Natalia Bailey is an Associate Policy Advisor in the Digital Finance Department at the IIF, where she focuses on the digital transformation of the financial system, particularly the application of new technologies such as Machine Learning to the domain of risk management, and financial sector supervision.
In her prior role she focused on banking prudential regulation where she reviewed the modeling practices in banks’ internal RWA models, and helped develop a multi-pronged approach to enhance internal model based capital approaches.
Natalia holds a MPP from George Mason University, and a BA in Economics from Hollins University, where she attended on an IIE-Fulbright Scholarship.

05 September

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Natalie Jakomis

Group Director of Data
GoCompare
Natalie Jakomis is Group Director of Data at GoCo Group , a multi-brand group that includes leading UK financial services, utilities and home services comparison website (GoCompare), global discount voucher website (MyVoucherCodes), automated energy switching service (weflip), market-leading energy comparison and switching service (energylinx) and leading automated energy savings service (Look After My Bills).
Natalie is included in the latest DataIQ™ 100 and leads GoCo Group’s data strategy, growing and enhancing a highly talented and multi-skilled data function and cultivating a sustainable data and analytics culture that supports the business vision. Natalie is passionate about providing decision-ready data and business intelligence strategies. She is experienced in developing and deploying data science solutions within a customer-centric business. Natalie is committed to driving a diversity and inclusion agenda in technology, particularly in Data Science.

06 September

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Newcombe Clark

Global Director, Rapid Learning Lab
AIG
Newcombe Clark is Global Director of the Rapid Learning Lab at AIG. He is primarily tasked with using advanced analytics, digital technologies, and big data sets to optimize the customer experience and drive profitable growth for AIG’s global partners. Newcombe came to AIG as a management consultant from The Cambridge Group, a growth strategy division of Nielsen, the audience measurement company. There he was tasked with using primary research merged with big consumer data sets to derive insights and innovation for major media and CPG firms. Before Nielsen, he worked for over a decade in private equity and commercial real estate investments. Newcombe holds degrees in Mechanical Engineering, Japanese, and an MBA, all from the University of Michigan.

05 September

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Noorjit Sidhu

Investor
Plug and Play Ventures
Noorjit is an investor at Plug and Play Ventures, based in Silicon Valley. One of his primary focuses is InsurTech, which has rapidly become one of the most active investment areas across Plug and Play globally. Current investments in the space include: Ople AI, WorldCover, Flock, Agentero, Halos, Surround, and Deepfraud AI.

06 September

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Paras Parekh

Head of Artificial Intelligence and Machine Learning
Credit Suisse
Paras Parekh is the Head of Artificial Intelligence and Machine Learning at Credit Suisse. He has a strong background in Business Intelligence and Advanced Analytics. Paras holds a Masters in Computer Science from Stevens Institute of Technology. In his spare time, he coaches his daughter's robotics team, who won the New Jersey VEX IQ State Championships for 3 straight years!

05 September

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Paula Sanders

Vice President, Global Customer Enablement
ABBYY
Paula Sanders is ABBYY’s Vice President of Global Customer Enablement and oversees all Sales Engineering activities worldwide. Prior to her international role, Paula was the Director of the US Sales Engineering Team. A leader by example, Paula quickly established a reputation for her understanding of ABBYY’s products – establishing unquestionable credibility with clients, prospects & VARs. Prior to joining ABBYY in 2011, Paula spent over 20 years in increasingly senior roles within the Document Imaging / Data Capture / ECM industry. Through her deep domain knowledge and broad industry experience, Paula has become a valuable asset to ABBYY for sales and delivery of high-value solutions to our customers.

06 September

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Peng Lee

Senior Data Scientist
American Family Insurance
Peng Lee is a Senior Data Scientist at American Family Insurance with over 13 years of industry experience. He is an accomplished machine learning scientist with a track record of building and deploying models that directly impact the company’s bottom line. He is passionate about understanding the latest machine learning research and how it can be leveraged. He holds a Bachelor of Mathematics from University of Wisconsin Madison and a Master of Predictive Analytics from Northwestern University. He is a Fellow of the Casualty Actuarial Society.

05 September

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Priya Sundararaman

Principal Data Scientist
State Farm
Priya Sundararaman is a Principal Data Scientist at State Farm. Priya has an undergraduate engineering degree in Computer Science and masters in Predictive Analytics with 16 years of industry experience. She is a pragmatic data scientist who believes that we are already in the midst of the fourth industrial revolution, with AI being a key enabler, permeating all aspects of business. At State Farm, she intends to make her contribution by using machine learning to solve hard business problems for demonstrable, measurable, and sustainable ROI.

05 September

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Rohit Chauhan

Executive Vice President for Artificial Intelligence
Mastercard
Rohit Chauhan is the Executive Vice President for Artificial Intelligence at Mastercard. He is responsible for establishing Mastercard as an AI powerhouse, leveraging and implementing AI at scale, providing Mastercard and its business partners with a foundational competitive advantage for the future. Prior to his current role, Mr. Chauhan led the Data & Analytics P&L for the organization. Mr. Chauhan joined Mastercard in 2006 from M&T Bank, where he led the Retail Risk Management function. Mr. Chauhan holds a Bachelor of Engineering degree from Government Engineering College, Jabalpur, India, a MS in Computer Science and an MBA from State University of New York at Buffalo.

05 September

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Ron Wu

Machine Learning Engineer
Morgan Stanley
Ron Wu is a Machine Learning Engineer at Morgan Stanley’s Wealth Management team, a group that leverages the latest technologies to build the most powerful machine learning tools in finance. He regularly participates in Hackathons and won many prizes developing applications and algorithms. He has a Master of Arts in Mathematics from NYU and spent six years as a Research Assistant at Columbia University Department of Physics.

06 September

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Saira Kazmi

Adjunct Professor
Connecticut College
Saira is a data-driven leader with extensive expertise in delivering innovative solutions for complex business problems from inception to production. Domains of experience include Bioinformatics, Medical Informatics, Healthcare, Insurance, Business Analytics, Text Search, Patent, and Intellectual Property Analytics. She is an advocate for metadata best practices and for establishing standards and business processes to enable high-quality data-driven metrics and actionable insights.

She received her Ph.D. in Computer Science with a focus in Bioinformatics from the University of Connecticut, and her post-doctoral training in Medical Informatics is from Yale University.

06 September

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Shi Yu

Chief Data Scientist
Vanguard
Shi Yu is a scientist who is enthusiastic about applying machine learning techniques to solve challenging problems. His main interests are Natural Language Processing, Large-scale Machine Learning, GPU/MPI Parallel Computing, and interesting problems in Finance and Insurance Industry. He is a Principal data scientist at Vanguard Group overseeing data science initiatives for Vanguard IIG, FAS and International. Shi holds a Master’s degree in Artificial Intelligence and a PhD in Electrical Engineering from K. U. Leuven. He finished his post-doc research at University of Chicago, and worked at IBM, Deloitte and American Family Insurance before joining Vanguard.

05 September

06 September

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Sophie Chen

Data Scientist
Nasdaq
Shihui (Sophie) Chen is a Data Scientist at NASDAQ’s Machine Intelligence Lab, a group dedicated to leveraging AI to improve financial markets and solutions. Her previous projects covered alternative data research, risk management, portfolio construction, and optimization. She holds a Masters of Finance degree from MIT.

06 September

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Sou-Cheng Choi

Research Associate Professor
Illinois Institute of Technology
In this introductory tutorial, we demonstrate basic techniques for building prediction models for financial asset prices. We start with sourcing online data for building a baseline time-series model, and then proceed to improve the model performance by iterative approaches that introduce feature engineering, hyperparameter search, record linkage, and interpretable machine-learning methods such as AutoML with LIME in Driverless.ai. We will evaluate model performance and if time permits, discuss simple investment techniques.

06 September

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Travis Whitmore

Quantitative Researcher
State Street
Travis Whitmore is a quantitative researcher in the Securities Finance Research team at State Street Associates (SSA). Since joining SSA in early 2018, Travis has helped develop and apply numerous quantitative models and contributed to several thought leadership pieces within the securities lending market.
Prior to joining SSA, he worked in State Street Global Markets as part of their rotational leadership program, where he developed collateral optimization models for the trading desks and built out an award winning application to help mitigate fraudulent behavior.
Travis interned with Morgan Stanley and several technology startups before he graduated from the University of Vermont with a Bachelor’s of Science Degree in Computer Science and Finance.

05 September

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Tucker Balch

Managing Director
J.P. Morgan AI Research
Dr. Balch is a Research Director at JPMorgan AI Research and a professor of Interactive Computing at Georgia Tech. He is interested in problems concerning multi-agent social behavior in domains ranging from financial markets to tracking and modeling the behavior of ants, honeybees and monkeys. He co-founded Lucena Research, an investment software firm that applies Machine Learning and Big Data approaches to investment problems. Balch has published 120 conference and journal articles. His work has been covered by CNN, New Scientist, Institutional Investor, and the New York Times. His graduated students work at NASA/JPL, Boston Dynamics, Goldman Sachs, Morgan Stanley, Citadel, AQR, and BlackRock.

05 September

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Tyler Lange

Corporate Partnership Manager
Plug and Play Tech Center
Tyler Lange is Plug and Play Insurtech's East Coast Lead, responsible for managing startup engagements with 12 major insurance carriers and brokerages based in greater-NYC. Headquartered in Silicon Valley, he was the 9th employee for the Insurtech practice that has since grown to be the largest of 15 verticals globally, with offices in Munich, Beijing, Tokyo, and Singapore.

06 September

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Tzvi Aviv

Founder & CEO
AgrilogicAI Inc.
Tzvi Aviv is an entrepreneurial scientist and innovation consultant working in Toronto. He founded AgriLogicAI to develop and commercialize artificial intelligence and machine learning software for profitability and sustainability in the agri-food sector. Tzvi has won many awards for his work, including seed funding from Next Canada and awards from agricultural and pharmaceutical companies. Prior to AgriLogicAI, he managed a drug development project at the Hospital for Sick Children and received a PhD in Medical Genetics from the University of Toronto. Additionally, he received an MBA from Ryerson University with a focus in the management of innovation and technology.

06 September

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Valentino Zocca

Vice President - Data Science
Citi
Valentino Zocca is Vice President at Citi and works in the Fintech Data Science group in New York. Having a Ph.D. in Pure Mathematics from the University of Maryland, and having worked, among other companies, at Boeing and at the US Census Bureau, Valentino’s expertise encompasses different industries and skills. At Citi he works in Fintech to improve understanding of customers’ financial needs and provide support for machine learning models aiming at improving customers’ financial decisions using different predictive models ranging from NLP to decision trees, clustering and neural networks. He is also a co-author of a 2017 book on Deep Learning titled “Python Deep Learning”.

06 September

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Viola Cao

Data Scientist
Zurich North America
Viola Cao, a Data Scientist at Zurich who graduated from the NYU Data Science Master's program with a focus on Deep/Machine learning and Big Data. Viola previously worked at Stanford University and the United Nations where she led the "Global Terrorism Analysis” project. She is also an accomplished badminton player and swimmer.

05 September

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Vishnu Narayanasamy

Senior Director II, Data Science, Advanced Analytics & Strategy
Liberty Mutual
Vishnu Narayanasamy is a Senior Director at Liberty Mutual and currently leads the Data Science team within the Global Risk Solutions Claims organization. Prior to this role, Vishnu spent 3 years in Liberty’s Global Retail Markets division, leading an advanced analytics team within the Marketing & Distribution. Before joining Liberty, Vishnu worked as a Management Consultant at Bain & Company and held various analytical positions at Capital One. Vishnu holds an MBA from the Tuck School of Business at Dartmouth and a Master’s in Engineering from Columbia University, New York.

06 September

06 September

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Yasser El Hamoumi

Assistant Vice President, Trading and Algorithmic Strategist (Securities Finance)
State Street
Yasser El Hamoumi is an Algorithmic Trader with State Street Global Market's Agency Lending program. He is responsible for the analytical framework, quantitative development, and implementation of algorithmic pricing. Additionally, Mr. El Hamoumi oversees the technical development of the lending program's market microstructures, which includes but is not limited to the lending program's electronic trading platform, analysis and research on broker trading behavior, intraday price discovery, and implementation of new trading technologies. Prior to his current role, Mr. El Hamoumi worked as a quantitative developer with State Street's Liquidity and Liability Management team. In this role, he was primarily focused with the development of data intensive models used for the management of State Street's liabilities. These projects include the design of the firm's operational deposit model, the quantitative estimation of credit lines with central banks and financial market utilities, and the execution of Federal Reserve Bank mandated stress testing. Mr. El Hamoumi attended Union College on the Posse Foundation Full Tuition Leadership Merit Scholarship and holds a Bachelor of Science in Mechanical Engineering.

05 September

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Zachary Glassman

Data Scientist
UBS

06 September

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Zachary Hanif

Senior Director of Machine Learning
Capital One
Zachary Hanif is a Senior Director at Capital One where he has developed machine learning capabilities across the enterprise. Currently focusing on Card Acquisitions, he led in the Center for Machine Learning where he developed a number of models for risk reduction and revenue generation and built towards the democratization of advanced modeling capabilities. Previously, Zachary focused on solving adversarial problems with a particular focus on cybersecurity and money laundering modeling. He is currently engaged in graduate studies in Computer Science at the University of Maryland, studying and building the next generation of large-scale data analysis capabilities.

05 September

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Shearman and Sterling

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IBM Services

Gold
IBM Services partners with the world's leading companies to reimagine and reinvent to build smart business -- from end to end and from both the outside in and the inside out. Our clients are optimizing processes with emerging technologies to deliver more intelligent workflows as an integral part of transforming into a cognitive enterprise. We partner with clients to design solutions, modernize and optimize their business, deliver sustained value and empower their people. Our end-to-end capabilities take clients from inception to support and are backed by the power of the full IBM portfolio. A new era for business has arrived. ibm.com/services/process
ReWork digital drop_FINAL.pdf Download Link
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ABBYY

Silver
ABBYY is a global provider of content intelligence solutions and services. We offer a complete range of AI-based technologies and solutions transforming business documents and content into business value. By providing digital transformation solutions to financial services, insurance, transportation, healthcare and other industries, ABBYY helps organizations achieve the next wave of growth. ABBYY technologies are used and licensed by thousands of international enterprises and government organizations, as well as SMBs and more than 50 million individuals. ABBYY has a worldwide presence with global offices in Europe, Russia and North America.
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Reality Engines

 "RealityEngines.AI is an AI research company that is focused on hard problems that enterprises face. These problems include: incomplete and noisy datasets, obtaining necessary talent, avoiding bias, and the black-box nature that often comes with creating such systems. We work on a number of research areas to solve these issues. Our research will be packaged into an easy-to-use, pay-as-you-go service accessible to all enterprises. In the meantime, we’re partnering with a few select organizations who apply it to their current problems."
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SumUp Analytics

SumUp Analytics is an artificial intelligence and natural language processing (NLP) company advancing the way businesses leverage unstructured, text-data. We provide a large-scale, high-speed text analytics platform that processes large data-sets and document collections in real-time, in multiple languages, and in unsupervised fashion. We enable users to extract key insights in an ultra-fast, efficient and transparent way. Our technology is trusted by Fortune 500 companies and Top 100 Hedge Funds. The product is available in SaaS and API formats, currently focused on applications in finance, social-media, legal, and government.
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Galaxy.AI

Galaxy.AI has built a deep learning engine for analysis of images to give real-time automated damage assessment for the property and casualty insurance industry. One of the primary industry verticals we work in is auto-insurance, where we make the claim estimation workflow for insurance carriers efficient by cutting the estimation time for claim appraisers by more than half and reducing the loss adjustment expenses by 70%. We work with national and international insurance companies that are among the top 10 general insurers in 5 countries.
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ComplySci

ComplySci is backed by Vista Equity Partners, a leading investment firm that exclusively invests in enterprise software, data and technology-enabled organizations led by world-class management teams.
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RE•WORK

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Mass Mutual- Sr. Data Engineer

Hiring
Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?
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Go Compare- Head of Data Science

Hiring
Would you like to lead a DataIQ award winning Data Science team that combines innovate state of the start automation with sophisticated algorithms to identify significant savings for our customers? Are you driven by the ability to deliver high-value data science initiatives across GoCo Group and actionable solutions across multiple brands?
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Go Compare- Data Scientist

Hiring
Would you like to work within a DataIQ award winning Data Science team that combines innovate state of the start automation with sophisticated algorithms to identify significant savings for our customers? Are you driven by the ability to take ownership of data projects that will create a more personalised, data-driven experience that will transform the tech comparison world for insurance, financial services and utilities?
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Mass Mutual- Data Architect

Hiring
Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?
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Mass Mutual- Data Modeler (Analytical Systems)

Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?
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Mass Mutual- Site Reliability Engineer

Hiring
Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?
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Mass Mutual- Cloud Ops Engineer

Hiring
Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?

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Mass Mutual- Head of Data Science Software Engineering

Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?

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Mass Mutual- Head of Customer Data Management Platform

Interested in joining a high performing team that creates knowledge from data and builds systems that solve hard problems?

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New York Life- Lead Data Scientist: Marketing

Hiring
A career at New York Life offers many opportunities. To be part of a growing and successful business. To reach your full potential, whatever your specialty. Above all, to make a difference in the world by helping people achieve financial security. It’s a career journey you can be proud of, and you’ll find plenty of support along the way. Our development programs range from skill-building to management training, and we value our diverse and inclusive workplace where all voices can be heard. Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and service, supported by our Foundation. It all adds up to a rewarding career at a company where doing right by our customers is part of who we are, as a mutual company without outside shareholders. We invite you to bring your talents to New York Life, so we can continue to help families and businesses “Be Good At Life.” To learn more, please visit LinkedIn, our Newsroom and the Careers page of www.NewYorkLife.com.
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New York Life- Program Manager: Centre for Data Science

A career at New York Life offers many opportunities. To be part of a growing and successful business. To reach your full potential, whatever your specialty. Above all, to make a difference in the world by helping people achieve financial security. It’s a career journey you can be proud of, and you’ll find plenty of support along the way. Our development programs range from skill-building to management training, and we value our diverse and inclusive workplace where all voices can be heard. Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and service, supported by our Foundation. It all adds up to a rewarding career at a company where doing right by our customers is part of who we are, as a mutual company without outside shareholders. We invite you to bring your talents to New York Life, so we can continue to help families and businesses “Be Good At Life.” To learn more, please visit LinkedIn, our Newsroom and the Careers page of www.NewYorkLife.com.
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New York Life- Senior Data Engineer

Hiring
A career at New York Life offers many opportunities. To be part of a growing and successful business. To reach your full potential, whatever your specialty. Above all, to make a difference in the world by helping people achieve financial security. It’s a career journey you can be proud of, and you’ll find plenty of support along the way. Our development programs range from skill-building to management training, and we value our diverse and inclusive workplace where all voices can be heard. Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and service, supported by our Foundation. It all adds up to a rewarding career at a company where doing right by our customers is part of who we are, as a mutual company without outside shareholders. We invite you to bring your talents to New York Life, so we can continue to help families and businesses “Be Good At Life.” To learn more, please visit LinkedIn, our Newsroom and the Careers page of www.NewYorkLife.com.
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StateFarm- Data Scientist

Hiring
For over 95 years, data has been key to State Farm. As a member of our data science team, you will work across the organization to solve business problems and help achieve business strategies. You will employ sophisticated, statistical approaches and state of the art technology. You will build and refine our tools/techniques and engage with internal stakeholders across the organization to improve our products & services.
Implementing solutions is critical for success. You will do problem identification, solution proposal & presentation to a wide variety of management & technical audiences. This challenging career requires you to work on multiple concurrent projects in a community setting, developing yourself and others, and advancing data science both at State Farm and externally.
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Anthem

Hiring
We are hiring for Junior and Senior level Data Scientists and AI EngineersMS/PhD in Computer Science, Applied Mathematics, Statistics or other quantitative fields with strong background/experience in Machine Learning and AIExperience with Deep Learning is preferredContact: Lakshmi Manohar Akella.
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Associate Validation Analyst- M&T Bank

Hiring
Responsible for review and validation of models used by various groups at M&T for varied purposes, including capital stress testing, risk measurement, pricing and profitability and management decision-making. This position will work under the supervision of team lead or manager to validate less complex models or less complex aspects of models.
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Raja Sarkar

Resume_Sarkar.pdf Download Link
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Sagar Jain

Resume_DataScience.pdf Download Link
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FinBit.io- Sales Professional NA

FinBit.io, a leading financial data aggregation and analytics company is looking for energetic sales professionals for North America region. If you like a fast paced startup environment then contact us at info@finbit.io.
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Hyperscience- Head of Machine Learning R&D

Hiring
http://jobs.lever.co/hyperscience/b5fc453-6683-4126-81d9-f3a719c753a
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Hyperscience- DevOps Engineer

Hiring
http://jobs.lever.co/hyperscience/283043fd-b7b4-43e0-ac8e-ddccc2ca99a4
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Introhive

AI in Finance
Introhive is the leading relationship intelligence and sales automation platform for enterprise. Customers across an array of roles and industries—from legal to accounting to commercial real estate and beyond—use Introhive’s automated data collection and AI-powered relationship visualizations to grow business connections, while saving time and money. Introhive’s platform integrates with most business technology (including CRM software, e-mail, and business intelligence) to reveal insights and eradicate manual data entry—boosting sales, revenue, technology adoption, productivity and data quality. Learn more at www.introhive.com.
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CORE

AI in Finance
As the undergraduate entrepreneurship society of Columbia University, Columbia Organization of Rising Entrepreneurs (CORE) works to inspire, educate, and connect the next generation of student entrepreneurs. CORE runs a wide host of events and programs to engage and immerse students in the startup and tech ecosystems and has hosted speakers such as Arianna Huffington, Jack Dorsey, and Peter Thiel. CORE also operates Columbia’s student startup accelerator, Almaworks--a program that has graduated participants into Y Combinator and Techstars--and is the student partner behind the Columbia Venture Competition as well as Startup Columbia, Columbia’s annual celebration of unconventional entrepreneurship.
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HyperScience

AI in Finance
HyperScience is a machine learning company focused on automating office work. Their solutions allow organizations to automate large amounts of manual data entry, even for documents with handwriting, poor image quality, or variable structure. HyperScience delivers automation solutions to Global 2000 companies and government institutions around the world, helping them focus on their core business by reducing bureaucratic burden on them and their customers.
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SortSpoke

SortSpoke helps enterprises turn unstructured documents into structured data, making it easy to automate the complex data entry work done by humans. This data can then feed your operational processes or analytics projects. It works especially well on unstructured documents in any language, doesn't require any programming or templates, and can be trained in less than 1 day.
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Levatas

Levatas is an AI solutions firm specializing in computer vision that drives innovation for its clients. Levatas is currently launching software for the P&C insurance world that will reduce fraud, automate inspections, and expedite claims. With over a decade of experience in pushing true digital transformation, Levatas helps businesses to tighten operations, improve process, make smarter decisions, and deliver a great customer experience.From identifying the right business cases to deploying deep learning models, Levatas has steadily built a team of experts who are making artificial intelligence a reality for forward-thinking businesses.
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Exhibition Area & Refreshment Breaks

REGISTRATION & LIGHT BREAKFAST

08:00 AM 09:00 AM

Registration will open from 8:15am, please have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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AI in Finance Summit
AI in Insurance Summit

WELCOME

09:00 AM 09:15 AM

The comperes for today are Gene Beidl, Data Scientist, FINRA (AI in Finance Summit) and Tucker Allen, Data Engineer, Chubb (AI in Insurance Summit)

Speakers

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AI in Finance Summit

Trends in Algorithmic Marketing and AI in Retail Finance - BARCLAYS

09:15 AM 09:40 AM

Advances in data science and machine learning redefined marketing by expanding the role of algorithms in key decisions within retail finance. Algorithmic marketing can be broadly summarized as: Applying programmatic approaches to automate evaluation of target-offer mix and optimization towards business objectives. While automation and autonomy go hand-in-hand, businesses realistically require timely analytical intervention and flexibility to adapt to internal and external market forces. In this talk we explore recent trends within AI enabled decision engines, present our journey towards exploration and implementation of such models and discuss key challenges, opportunities in the world of retail finance data science.

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AI in Insurance Summit

AI to Advance the Insurance Value Chain - SWISS RE

09:15 AM 09:40 AM

Artificial Intelligence (AI) is transforming many industries, including insurance. AI, as used here, refers to a collection of modeling methodologies, tools and platforms, applications, big data utilization, and the thought process for problem solving in the field of Artificial Intelligence. As insurance is a highly specialized industry, the AI adoption needs to be customized. However, we have already seen many opportunities and high potential applications. This talk will discuss how insurers are leveraging the latest AI techniques across the insurance value chain, from sales & marketing, underwriting, costing/pricing, business management, to claims handling.

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AI in Finance Summit

Federated AI for Banking and Finance - IBM

09:40 AM 10:00 AM

Developing high quality AI tools and applications requires high quality data. The success of Google and Facebook is due to their access to the very large volumes of data generated by the use of their products. The business of banking and finance generates large amounts of data, but access runs into both inter- and intra-company barriers. This talk will use a simple lending example to illustrate the needs, and then discuss some emerging technologies, which we call Federated AI, that show promise in addressing the barriers without compromising privacy and trust.

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AI in Insurance Summit

Machine Learning & The Future of Insurance Product Development - HAVEN LIFE

09:40 AM 10:00 AM

The insurance industry faces a number of challenges in developing new products: long-term liabilities, a rapidly changing distribution environment, and complex customer behaviors that can materially impact product value. At Haven Life and MassMutual, we believe that the actuarial paradigm must evolve in order to support innovation and continue to delight customers for the next 100+ years. We are building a new product development platform that uses machine learning models and modern econometric techniques to drive rapid and sound product development and pricing for the challenges of the modern insurance market. Key Takeaways: 1) Most of the focus on AI and ML in insurance has been on customer-facing experiences like underwriting and marketing. A significant opportunity with AI and ML is actually on the back end, such as product design and pricing. If you’re not improving operations behind the scenes, which AI and ML can do, then you’re just treating symptoms and not addressing the actual problem 2) Long term behavior is hard to predict. Designing products proactively can help make some customer outcomes easier to model, easier to predict, and more accessible to your customers 3) We exist in an industry dominated by legacy technology, and actuaries have learned how to make yesterday’s tools work for a long time. That strategy isn’t going to work anymore. We need to move forward and adopt toolsets that are actually built for the challenges was are trying to meet.

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AI in Finance Summit

Developing ML-Driven Customer-Facing Product Features - SQUARE

10:00 AM 10:25 AM

As Machine Learning becomes a core component of any forward-looking company, how can we weave ML-driven functionality into the products and services we offer? This talk will explain the methodology we follow at Square when developing ML-driven customer-facing product features, which is based on paying close attention to four key and interdependent aspects: Design, Modeling, Engineering, and Analytics. Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience. Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and —crucially for a customer-facing product— how the application behaves in the face of the mistakes the model will inevitably make (false positives, false negatives, lack of predictions above a certain confidence). Engineering in turn is concerned about running the ML model at scale, and thus cares about the latency, throughput, and robustness of the inferencing service. Finally, Analytics is concerned about the adoption of the feature, and thus cares about the instrumentation to capture detailed usage, the definition of success metrics and dashboards, and the collection of feedback in a manner that the ML model can learn from, and thus keep improving over time. When all these aspects align, we can create remarkable ML-powered experiences that delight our customers. Key Takeaways: 1. AI/ML can power remarkable functionality in the fintech space; 2. You need to pay close attention to the Design, Modeling, Engineering, and Analytics of the product feature; 3. Then you will delight your customers

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AI in Insurance Summit

Deep Learning in Insurance Beyond Niche Applications - PRUDENTIAL

10:00 AM 10:25 AM

Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.

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Exhibition Area & Refreshment Breaks

COFFEE

10:25 AM 11:15 AM

Help yourself to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Track

DEEP DIVE: New to AI in Finance & Insurance? Time to Ask Qs!

10:30 AM 11:10 AM Hub One

Roundtable Discussions with Industry Leaders in Finance and Insurance! Join this morning's speakers from the AI in Finance Summit and the AI in Insurance Summit to ask all of the questions that we couldn't quite get to in the Q&A! Participants include Anurag Setty (Barclays US), Marsal Galvalda (Square), Boyi Xie (Swiss Re) & Michael Natusch (Prudential)

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AI in Finance Summit

Anomaly Mining: Detection, Explanation, Interaction - CARNEGIE MELLON UNIVERSITY

11:15 AM 11:35 AM

Anomaly mining is a key unsupervised learning task, with numerous applications in finance, security, surveillance, etc. Despite its importance and extensive work on the topic, anomaly mining remains a challenging subject in part due to the tremendous variety of both the forms that anomalies can take and the settings in which they are to be identified. One of the main thrusts of my research has been in tackling these challenges in anomaly mining by building models that are suitable for different practically-relevant settings. In this talk, I will highlight some vignettes from my recent work on streaming, contextual, and relational anomaly detection with concrete applications to intrusion, ad fraud, tax and credit card fraud detection. I will also discuss how to improve detection quality by bringing human-in-the-loop, with a focus on auditing systems. Finally I will move beyond detection and introduce new approaches for explaining anomalies toward verification and sense-making. Key Takeaways: 1 - Context and relational data matters for various fraud detection settings. 2- We can improve detection quality using interactive/human-in-the-loop techniques. 3- Explaining the anomalies is as important as detection.

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AI in Insurance Summit

Machine Learning to Specialty Insurance Underwriting - AXIS CAPITAL

11:15 AM 11:35 AM

Artificial Intelligence (AI) is transforming the specialty/commercial insurance underwriting from a ‘detect and repair’ to ‘predict and prevent’ approach. Fueled by the massive amount of data, AI now delivers more accurate and real-time risk assessment which helps underwriters efficiently acquire information,prepare more accurate quotes, and speed up and even automate the underwriting process. This talk presents the data challenges in specialty insurance underwriting and demonstrates the application of reinforcement and supervised machine learning techniques on our risk selection, underwriting, and submission prioritization models.

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AI in Finance Summit

Financial Applications of Reinforcement Learning - NEW YORK UNIVERSITY

11:35 AM 12:00 PM

This talk will present a brief overview of methods of reinforcement learning including the following topics: * Reinforcement learning and applications for portfolio optimization and option pricing * inverse reinforcement learning and its financial applications

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AI in Insurance Summit

Uncover Hidden Patterns in Financial Crime Activities Using Graph Analytics - MANULIFE

11:35 AM 12:00 PM

Financial crimes in fraud and money laundering spaces are common problems faced by financial institutions. With the use of data analytics and machine learning in recent years, the effectiveness of advanced technologies in financial crime detection has been proven. However, the most commonly used techniques have limited capabilities in discovering underline crime patterns. This presentation focuses on how to leverage graph databases and link analysis to detect and prevent financial crime activities by uncovering hidden patterns in data, using graph algorithms in real-time.

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AI in Finance Summit

Applications of Academic Theory and Quant Techniques in Securities Lending - STATE STREET

12:00 PM 12:25 PM

Stocks that are heavily shorted and go “special” in the securities lending market can exhibit interesting behavior that is different from stocks in more “normal” regimes. However, it can be difficult to identify and model events when stocks might be subject to these short-market pressures in the securities lending space. In this presentation, we discuss a research project that draws from academic literature, market insights, and quantitative techniques to arrive at differentiated insights on these “special” events. We discuss our approach to event classification predictive models, such as linear regression, time series forecasting, and k-nearest neighbors classifications, and the implications of the predicted cross-sectional behavior in our universe.

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AI in Insurance Summit

Detecting Claims Fraud Using Text Data & Bi-Directional LSTM Networks With Attention - AMERICAN FAMILY INSURANCE

12:00 PM 12:25 PM

According to the Insurance Information Institute, fraud accounts for about 10 percent of incurred losses and loss adjustment expenses, costing property-casualty insurers an estimated $30 billion each year. Ultimately, this cost is passed on to the consumer in the form of higher insurance premiums. The focus of this presentation will be to discuss how bi-directional long-short term memory (LSTM) networks with attention can be applied to claims textual data, to flag and reduce claims fraud. I will discuss the business context, technical solution, as well as ongoing and future work. (1) understand SIU process and where ML can be applied (2) how to apply ML to text (3) business impact of ML

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Exhibition Area & Refreshment Breaks

LUNCH

12:25 PM 01:30 PM

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers and exhibitors.
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Deep Dive Track

DEEP DIVE: Lunch & Learn

12:30 PM 01:25 PM Hub One and Hub Two

Grab some lunch and join fellow attendees during informal roundtable discussions in Hub One & Hub Two. What was your highlight of the morning? What surprised you? What are you keen to learn more about?

Speakers from State Street and Levatas will be hosting a couple of tables for discussions!
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AI in Finance Summit

Recent Developments in Options Hedging - CONCORDIA UNIVERSITY

01:30 PM 01:50 PM

Traditional hedging procedures are based on local sensitivities of risk factors called Greeks. However, such myopic approaches do not consider the interaction of hedging errors through time. Global hedging procedures, which are expressed as optimization problems, consider such interactions. These algorithms received recent attention in the literature. Such procedures relying on machine learning algorithms such as dynamic programming and reinforcement learning have the potential of greatly outperforming the traditional Greeks-based hedging, especially for long-dated options. Key Takeaways: 1) A new approach for hedging has emerged. 2) It has ties to machine learning. 3) It can substantially outperform traditional methods in specific cases.

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AI in Insurance Summit

AVIVA’s Applied AI/ML Journey - AVIVA

01:30 PM 01:50 PM

The rise of AI in the last decade places it as a key advancement for the fourth industrial revolution.  AI today has resulted into consumer and business applications driving measurable outcomes.  However, at-scale application of AI within a complex and multi-faceted business such as Insurance requires a holistic re-think of people, processes and technology.  In this talk, I outline Aviva's global journey of embedding AI within our business and realizing significant and measurable gains.  From machine-learning based pricing algorithms to AI-based underwriting, Aviva is now at the forefront of bringing this revolution to our industry.

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AI in Finance Summit

Identification of Investment Decision Process Using Online Inverse Optimization - VANGUARD

01:50 PM 02:10 PM

We consider asset allocation as an optimal decision process governed by Markowitz Framework and Black-Litterman model. Under this framework, several important decision variables, such as risk aversion, expected portfolio benchmark return, and expected asset return, can all be learned by inverse optimization process. In this talk, we investigated a novel online inverse optimization framework on portfolio data, market news and asset price data to identify decision variables in investment decisions. We showed how those learned variables can be used to understand investor’s decisions, fund managers’ advices and recommendations, and ultimately enhance automated financial advice capabilities.

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AI in Insurance Summit

Deep Learning for Property Visual Features & Extraction Tools - ZURICH NORTH AMERICA

01:50 PM 02:10 PM

Tricks and traps on how Machine learning, AI and Computer Vision are utilized to analyze multiple image types in order to build a database of all buildings in the globe and extract property features to enrich the inputs for risk assessment and pricing models.

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Deep Dive Track

DEEP DIVE: Exploring AI for Financial Compliance - COMPLYSCI

01:50 PM 02:30 PM Hub One

Technology is rapidly advancing, changing the way people work, entertain and communicate. This is especially true for compliance professionals working in financial services where changes to surveillance and oversight expectations and techniques are happening at a rapid pace. Artificial Intelligence and Machine Learning are being introduced to the industry and are positioned to make rapid changes to compliance in the near future. In this interactive session, Jean-Marc Levy, CEO of ComplySci, will dive into the expected changes that AI will bring to compliance for financial firms and how you can prepare your teams for the changes ahead. Key Takeaways: Data driven compliance tools are becoming increasingly important to compliance teams. The role of these teams is evolving to include more responsibilities and they need to leverage advanced technology tools, like AI/ML and predictive analytics, to shift their focus from hindsight to insight to foresight. Better tools and data analysis can help teams build a culture of compliance across the organization.

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AI in Finance Summit

How To Set Up A Successful AI Center of Excellence Within Global Markets - CREDIT SUISSE

02:10 PM 02:30 PM

Setting up a center of excellence within a financial institution can be very challenging but at the same time can be extremely rewarding. Paras Parekh will be presenting 4 steps that each organization can implement for a successful deployment of a center of excellence

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AI in Insurance Summit

Mindset Segmentation Prediction for Affluent Population - MASS MUTUAL

02:10 PM 02:30 PM

Customers at MassMutual are defined within 5-segment attitudinal segmentation framework. In order to run marketing campaign on affluent prospective customers, business stakeholders asked Data Science to predict mindset segments for prospects within certain age, income, and net worth criteria. Third party vendor data provided the mindset segmentation framework, then this data was matched to out prospect database. The models were learned from matched data and applied to unmatched data. To address class imbalance issues, SMOTE tool was applied. The best model was based on Random Forest algorithm via the sklearn package. Key Takeaways: 1) Any market segmentation framework relies on segment distribution 2) Sometimes, predicted segments are highly unbalanced 3) Resampling (specifically SMOTE) solves the problem.

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AI in Finance Summit

The Age of AI at Mastercard - MASTERCARD

02:30 PM 02:50 PM

Over the next few years, there won’t be a single sector of the economy untouched by AI. It will massively increase the value and speed of data transactions, interactions, and decisions. AI is therefore critical to the business of the future – building systems through AI will be essential for remaining competitive. Each day, we interact with AI, most often without even knowing it – from the use of transportation apps, algorithms that serve up product recommendations, to autonomous cars and chatbots in customer service. AI will soon be thought of like electricity – the undercurrent to our daily activities. Mastercard has been using AI to process as many as 75 billion transactions each year as the world’s fastest payments network. AI is a critical tool in the fight against fraud, but we are expanding its use even further to: Power Mastercard products with AI, Apply AI to Mastercard operations to make the company more efficient and effective, Assist our customers to develop solutions that harness the power of AI

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AI in Insurance Summit

Deep Learning for Medical Chart Classification - ANTHEM

02:30 PM 02:50 PM

Knowing the evidence of a disease as a current condition for a member is critical for areas like risk adjustment, planning interventions, better personalization, etc that might not always be evident from claims data alone. A member’s present health condition can be in the EHR (Electronic Health Record) in the form of doctors notes that may not be in claims data for a time period. Diseases mentioned in EHR can be in different contexts like family history, past conditions, etc. The talk will present our work on attention based deep learning approaches for classifying a chart for a health status in the context of a current condition

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AI in Finance Summit

The Cognitive Enterprise: Revinventing Your Company With AI - IBM

02:50 PM 03:10 PM

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AI in Insurance Summit

Using Machine Learning to Maximize Profitable Revenue - AIG

02:50 PM 03:10 PM

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Exhibition Area & Refreshment Breaks

COFFEE

03:10 PM 03:55 PM

Help your self to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Track

DEEP DIVE: Talent & Talk

03:15 PM 03:50 PM Hub One

Are you hiring or seeking a new role? Due to popular demand, we are returning with Talent & Talk which is your chance to share a 1-minute pitch about your vacancies or about yourself if you are ready to start a new chapter.
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AI in Finance Summit

PANEL: How Do We Ensure The Ethical Development of AI in Finance? - CAPITAL ONE, UNIVERSITY OF PENNSYLVANIA, SHEARMAN & STERLING LLP, INSTIUTE OF INTERNATIONAL FINANCE

03:50 PM 04:30 PM

This panel discussion is focused on exploring the ethical use of AI in Finance and the considerations that must be made within this sphere. Questions that will be answered include:  How can we harness the capabilities of AI whilst mitigating the risks from the unethical use of data? Could the use of AI in Finance have damaging outcomes for customers? How can we ensure that AI in finance is being developed and applied fairly, for all? How can we reduce the opportunity to exploit behavioral biases via AI in finance? What is the scope for bias in AI in Finance? Are models being developed in a diverse manner? 

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AI in Insurance Summit

Safety First: AI To Detect Distracted Driving - STATE FARM

03:55 PM 04:20 PM

Distracted driving is one of the leading causes of auto accidents, according to the National Highway Traffic Safety Administration (NHTSA). This talk will demonstrate the use of Artificial Intelligence to analyze driver images and identify distracted driving behavior autonomously. Two deep learning models were created using videos of drivers from a 3D image sensor and a 2D web camera. An ensemble of the models was used to classify the action of the driver. I will discuss the methodology, results and suggested areas of future work to improve driver safety. Key Takeaways: 1. Self-Driving cars and distracted driver detection can together provide a great driving experience. 2. We were able to identify distracted driving behavior with high accuracy, in real-time. 3. Future work - this can be integrated with self-driving car technology for a safe, fun driving experience.

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AI in Insurance Summit

PANEL: Assessing Privacy & Regulation in AI Development & Deployment in Insurance - PRUDENTIAL, PACE UNIVERSITY, SIA PARTNERS, BAKER & HOSTETLER

04:20 PM 05:00 PM

This panel discussion will explore the key considerations for implementing and working with AI in Insurance including the best practices for sourcing, manipulating and using data as well as the ethical, governance, legal & regulatory implications involved with these practices. Questions that will be addressed include: What are the legal risks involved in AI development & deployment? What do businesses need to be most aware of? What are common pitfalls/oversights in this area? How is rapid AI adoption affecting individuals/customers privacy? How can we ensure that privacy is upheld during this process? Is current regulation enough to ensure the privacy of individuals affected by the application of AI in Insurance? How does this differ from other sectors? What is the current regulatory response to the insurance industry’s push for automation & AI? How does this differ internationally? How do you expect this to change going forward as AI dissipates more areas of Insurance?

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AI in Finance Summit

J.P. Morgan AI Research: Who We are and What We’re Doing - J.P. MORGAN

04:30 PM 05:00 PM

J.P. Morgan AI Research is celebrating its first year.   In this presentation I will tell you about this new group, how we are different from AI groups at most other financial institutions, and about the projects we’re working on.  I will focus especially on our efforts in simulating high frequency markets.

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Exhibition Area & Refreshment Breaks

CONVERSATION & DRINKS

05:00 PM 05:55 PM

Join us in the foyer area and grab a drink to celebrate the end of Day 1 and continue to network with attendees.
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Exhibition Area & Refreshment Breaks

End of Summit

05:55 PM 06:00 PM

Thank you for attending! Doors open at 8am tomorrow morning. See you tomorrow for more discussions, presentations and networking! And please remember to bring your badge! :)
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Exhibition Area & Refreshment Breaks

REGISTRATION & LIGHT BREAKFAST

08:00 AM 09:00 AM

Registration will open from 8am, please have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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AI in Finance Summit
AI in Insurance Summit

WELCOME

09:00 AM 09:15 AM

The comperes for today are Shi Yu, Chief Data Scientist, Vanguard(AI in Finance Summit)

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AI in Finance Summit

Build Relationship Capital and Grow the Bottom Line with AI - INTROHIVE

09:15 AM 09:35 AM

Financial services firms around the world are using artificial intelligence (AI) to streamline business processes and solve complex challenges, like credit fraud detection and malware classification. And now, AI that builds rich, at-a-glance relationship maps is revolutionizing how firms manage and grow relationship capital—and the bottom line. In this session, you’ll learn what relationship capital and relationship mapping are, and find out what’s possible when you can easily visualize who in your firm knows who—and how well. Attendees will also get a look at best practices and case studies highlighting how Introhive’s client firms’ business development teams use the valuable insights garnered from relationship mapping to increase productivity, make data-driven decisions, and attract and grow new business. Key Takeaways: Understand what relationship capital is. Learn what is possible when you can easily visualize who in your firm knows who. Best practices when using relationship intelligence and data automation to increase productivity, make data-driven decisions, and attract and grow new business.

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AI in Insurance Summit

AI in Commercial Claims - LIBERTY MUTUAL

09:15 AM 09:35 AM

AI within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service around the clock to an array of machine learning tools that enhance the critical thinking skills of the adjusters. To reap the full range of benefits, insurance companies need to devise an enterprise-level strategy to implement AI in such a way that it offers a more integrated experience to both its customers and its employees. This talk will focus on using AI and Machine Learning in different steps of claims processing from intake to claims management and closure.

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Deep Dive Track

DEEP DIVE: Explainable Automated Machine-Learning for Forecasting Financial Asset Prices - ILLINOIS INSTITUTE OF TECHNOLOGY

09:15 AM 10:15 AM Hub Two

In this introductory tutorial, we demonstrate basic techniques for building prediction models for financial asset prices. We start with sourcing online data for building a baseline time-series model, and then proceed to improve the model performance by iterative approaches that introduce feature engineering, hyperparameter search, record linkage, and interpretable machine-learning methods such as AutoML with LIME in Driverless.ai. We will evaluate model performance and if time permits, discuss simple investment techniques.

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AI in Finance Summit

Next Generation NLP for Finance - SUMUP ANALYTICS

09:35 AM 09:55 AM

Our long experience at SumUp Analytics has clearly revealed that NLP users have common characteristics including a need to identify novel content, value what makes it novel, and overcome language barriers. We discuss how NLP and next generation text analytics has made significant progress in addressing these challenges. Finance professionals can now leverage these new technologies to consume large-scale text information more quickly, efficiently, and comprehensively. 80% of data is unstructured (in the form of text), growing at 50% per year. While it is overwhelming & comes with many challenges, many financial professionals recognize the opportunity to leverage text-data. We have worked with clients ranging from financial services companies to social media companies to the U.S. government on how they're incorporating text-data into their research, compliance, risk processes. We provide 3 practical applications of NLP/text-data where clients have seen success including researching trading signals from text data, incorporating NLP to increase efficiency in compliance process, and using text-data to build ESG investment products. Key Takeaways: 1) The sheer scale of today’s data makes it increasingly more of a liability than an asset. Current methods cannot support, timely, efficient and thorough knowledge extraction. 2) Recognizing that current approaches struggle to meet today's requirements and cannot scale, SumUp developed a new paradigm, enabling the near-real time realization of actionable insights and knowledge from data at scale. 3) Our approach dramatically changes how those who rely on timely insights derived from constant streams of disparate data in a variety of languages can work now and into the future.

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AI in Insurance Summit

Loss Prediction in Crop Insurance - AGRILOGIC AI

09:35 AM 09:55 AM

AgriLogicAI helps farmers and crop insurance companies mitigate weather and climate change risks by applying deep learning and machine learning to satellite images and historical farm data. We predict grain yields within season across areas ranging from single fields to entire states. Currently, we are utilizing data from thousands of corn farms across Indiana to predict crop yields and loss risks using satellite imagery, weather, and soil data. Our technology can improve actuarial risk models, automate on-boarding, automate claim processes, and improve financial planning in crop insurance companies.

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AI in Finance Summit

Text Classification with Small Datasets Using Deep Transfer Learning - UBS

09:55 AM 10:20 AM

Massive quantities of domain specific labeled data have been the fuel, but also the primary bottleneck for using deep learning algorithms in industry. Organizations which lack the budget, time or have data privacy issues face hurdles in collecting such large amounts of domain specific human annotated data. I will review and compare methods to tackle this problem for text classification tasks via transfer learning using deep learning models. The models discussed will include Universal Sentence Encoders, ELMo and BERT. I will describe the model architectures used, specifics of training mechanisms, the evaluation criteria that guided the experiments and finally provide an attribution analysis of which components contributed most to end performance results.

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AI in Insurance Summit

Using Deep Learning to Reduce Wildfire Risk - TRAVELERS

09:55 AM 10:20 AM

Wildfires can pose particular dangers to both lives and property because they often begin unnoticed and can spread quickly. According to the Insurance Institute for Business & Home Safety, windborne embers present a significant threat from wildfires and are the primary cause of most building ignitions. Advances in Deep Learning have shown steps that can be taken to identify these threats.

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Exhibition Area & Refreshment Breaks

COFFEE

10:20 AM 11:05 AM

Help your self to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Track

DEEP DIVE: Investing in FinTech & InsurTech AI Startups

10:20 AM 11:00 AM Hub One

VC Panel and Networking with Investors - get your questions answered!

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AI in Insurance Summit

AI and Risk Assessment Using Health and Wellness Applications - OPTIMITY

11:05 AM 11:30 AM

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AI in Finance Summit

Smart Index Management With AI - NASDAQ

11:10 AM 11:35 AM

The financial service industry has been asking the same question for the last decade: how to provide the same level of performance with a much lower cost. In response to this, the global capital markets witnessed an aggressive growth of the passive investment. Its market share is more than doubled in the past 10 years. Our team will explain the challenges of smart index management, and illustrate our application of advanced analytics, machine learning, and optimization for smart portfolio construction. Key Takeaways: Constraint handling in portfolio optimization, challenges to apply ML in financial context, optimization in finance

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AI in Insurance Summit

Deep Learning for Automated Claims Processing in Car Insurance - GALAXY.AI

11:30 AM 11:55 AM

Galaxy.AI has built a deep learning engine for analysis of images to give real-time automated damage assessment for the property and casualty insurance industry. The first industry we are targeting is auto-insurance, where we make the claim estimation workflow for insurance carriers efficient by cutting the estimation time for claim appraisers by more than half and reducing the loss adjustment expenses by 70%. Our product has potential of saving up to $18M annually for a large insurance carrier that transacts 300K claims every year on average. We work with national and international insurance companies that are among top 10 general insurers in 3 countries, US, Australia and India, and we have a global pipeline with insurers in North America, Europe and Asia. Our product is in live deployment with national insurance carriers. 3 Key takeaways: 1) AI and computer vision leveraged to get high-quality input 2) Highly accurate damage detection needed to calculate the accurate claim estimate 3) Leverage experts to train the AI and address edge cases

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Deep Dive Track

DEEP DIVE: AI Powered Real-Time Analytics on Exchange Traded Options - NASDAQ

11:30 AM 12:00 PM Hub Two

With a recent and dramatic rise in interest between option and equity markets and how they interact, (viz. market completeness and lead-lag relationship), delta one market participants start to realize the usefulness of derivatives market activities in equity trading. One of the most prevailing topics is forecasting the direction of stock price movement through option data analysis. In this session, we will walk through our end-to-end research process and illustrate quantitative finance/machine learning techniques we applied to extract embedded value from exchange-traded options transaction records. Key Takeaways: 1. extract useful insights from large-scale, noisy data. 2. ML practice on time series data. 3. feature engineering on real-time options data

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AI in Finance Summit

Machine Learning and Cognitive Automation for Documents - Extending the Value of your BPM and RPA investments - ABBYY

11:35 AM 12:00 PM

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AI in Insurance Summit

Automating Claims in the Pet Health Insurance Space - TRUPANION

11:55 AM 12:20 PM

Trupanion is now automating 30% of its electronically submitted claims using machine learning models. As a result, invoices are paid directly to the veterinarian within seconds, rather than reimbursed to the policy holder after several weeks. A few of the challenges we’ve overcome in this project should be general enough to be useful in other applications. I’ll be covering the following topics in my talk: Methods for attaching medical meaning to free typed invoice line items, Replicating human decision making, Pain points of cross-departmental product teams , Communications addressing the implications of automating someone’s job

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AI in Finance Summit

Coincident Indicator for the Colombian Economy - BANCOLOMBIA

12:00 PM 12:25 PM

Financial markets are very sensitive to macroeconomic data published on a daily basis by official and non-official institutions. For a developing financial market like the Colombian, a few activity indicators have a stronger impact on the fluctuations of asset prices, and therefore, financial agents are always looking for the best predictive models to anticipate the movements of GDP growth, inflation, and employment. Due to the nature of such indicators, DANE (the National Department of Statistics) publishes its several activity indicators with some lag. Quarterly GDP growth, for instance, has a publication lag of two months. In this project we propose a coincident activity indicator that mimics the evolution of the ISE (economic activity indicator), published monthly by DANE (with a lag of 2.5 months). We construct the inputs for this indicator by efficiently processing and leveraging the large amount of transactional data that our customers generate on a daily basis (credit card payments, utilities payments, POS purchases, credit payments, transfers among investment funds, etc.). Based on the economic activity of the merchants and firms involved, we are able to nowcast, at the industry level, the activity for the whole economy on a monthly basis. The modeling approach uses a Bayesian Model Selection framework for selecting the best predictive models. Live testing has shown, so far, an excellent fit for the global model. Key Takeaways: Leverage of massive transactional data to predict macro variables, forecasting economic activity with ML algorithms, prototype data-based solutions for bank customers

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AI in Finance Summit

LUNCH

12:25 PM 01:25 PM

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers and exhibitors.
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Deep Dive Track

DEEP DIVE: Lunch & Learn - COLUMBIA UNIVERSITY

12:30 PM 01:20 PM Hub One

Roundtable Discussions with CORE - As the undergraduate entrepreneurship society of Columbia University, Columbia Organization of Rising Entrepreneurs (CORE) works to inspire, educate, and connect the next generation of student entrepreneurs. CORE runs a wide host of events and programs to engage and immerse students in the startup and tech ecosystems and has hosted speakers such as Arianna Huffington, Jack Dorsey, and Peter Thiel. CORE also operates Columbia’s student startup accelerator, Almaworks--a program that has graduated participants into Y Combinator and Techstars--and is the student partner behind the Columbia Venture Competition as well as Startup Columbia, Columbia’s annual celebration of unconventional entrepreneurship.
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The Role of ML in Anti-Money Laundering - J.P MORGAN

01:25 PM 01:50 PM

As financial institutions face increasingly complex money laundering activites, traditional rules-based AML systems are becoming less efficient for real-time monitoring and detection. This talk will highlight the current challenges faced by banks in this space and discuss how Machine Learning algorithms can be used to 'learn' from past behaviors to increase efficiency and identify previously undetected transactional patterns and relationships.

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AI in Insurance Summit

Developing Innovative Data Science Solutions to Increase Customer Retention - GOCOMPARE

01:25 PM 01:45 PM

Customer retention is the set of actions that companies take to stop customers from leaving and to retain and grow as many as possible into loyal customers. Customer retention starts with the first customer interaction and continues throughout the customer’s entire relationship with your organisation. This talk will share information about the exciting journey GoCo group is on and the opportunities we have found to  improve customer retention across its multiple brands.

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Deep Dive Track

DEEP DIVE: Near Real-Time Data: The Beauty of Pub/Sub Systems and Getting Started with Kafka - CHUBB

01:30 PM 02:05 PM Hub Two

Every machine learning project is different, but all of them rely on the same thing: data. Without it, an AI project is just an idea. The success of some projects might rely on "Near real-time data", a term that has joined the elite ranks of "cloud" and "big data" in recent buzzword canon. But what is it that makes near real-time data pipelines so different from traditional methods? We'll begin with a brief explanation of the Publish/Subscribe (Pub/Sub) method of data transmission and how it enables us to ingest, process, transform and emit thousands of messages, or more, per second. We'll walk through a basic, single node, Kafka implementation together, and I'll share some of the stumbling blocks we experienced at Chubb during our own Kafka rollout. *Laptop Recommended Laptop for the Hands-on Walkthrough portion, but not required*

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AI in Insurance Summit

Metadata is the Key to AI - CONNECTICUT COLLEGE

01:45 PM 02:05 PM

Building solutions that embed machine learning algorithms to deliver insights into business workflows requires the same rigor as in other scientific practices. Adopting best ideas from conducting "Reproducible Science" enables credibility, and explainability, especially when dealing with highly-regulated industries like Medicine, Insurance, or Finance.  This talk focuses on the vital ingredient necessary for enabling AI - accurate, secure, and reliable data, which in turn is enabled by retaining precise metadata about instruments, methods, touchpoints, lineage, and reference data. 

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AI in Finance Summit

The Future of High Wealth Individuals Investment Advisors - MORGAN STANLEY

01:50 PM 02:15 PM

Chatbots are ubiquitous in customer service. Despite the research and development in AI, recent data shows that majority of customers prefer to chat to a real human rather than a machine. Imagine building a chatbot for high wealth individuals. It won’t be your average Chatbot especially if it is coming from the world’s largest wealth advisor, Morgan Stanley. The Chatbots we are building at MS are tailored for every individual; hundreds of hours are spent analyzing our client’s behavior and personas to build an advisor that custom fits their needs and investment strategies. In our session, we will walk through our process of building the world’s most advanced financial Chatbots.

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AI in Insurance Summit

AI & Machine Learning Model Production & Operation at Scale - NEW YORK LIFE

02:05 PM 02:30 PM

Historically, employment of data science techniques in FinTech has been limited to a small, elite group of actuaries and “quants” who, quite frankly, seemed a mystery to many of us. Today, Artificial Intelligence is all the rage. Recent years have seen a push to educate the workforce on how to incorporate AI when solving business problems. And now, the demand is here! New York Life shares its journey toward operationalizing machine learning and preparing to scale for the future. The case study covers the evolution of business and technical capabilities, a description of the business problem and subsequent solution design.

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AI in Finance Summit

PANEL: How Can We Overcome Obstacles to Capturing ROI in AI & Machine Learning? - SOCIETE GENERALE, CITI, UBS

02:15 PM 03:00 PM

This panel discussion aims to explore the current opportunities and challenges in terms of capturing ROI when developing and deploying AI for enterprises of varying sizes. Are there particular areas that could be improved in the AI pipeline? How can we optimize engineering, infrastructure, culture, teams and decision making to see increased ROI (higher revenue, cost reduction, operational efficiency, value gains)?

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AI in Insurance Summit

PANEL: Considering Opportunities & Challenges for the Future of AI in Insurance: What’s Next? - INSURANCE INSIDER, LIBERTY MUTUAL, NORTHWESTERN MUTUAL, TRUPANION

02:30 PM 03:00 PM

2019 has witnessed rapid advancements in AI adoption within the Insurance sector. The purpose of this panel discussion is to predict how this will continue into the future. Subtopics include; What areas of insurance are set to be altered the most by AI & ML? What are the main barriers still in overcoming legacy systems? In what insurance area are innovations expected to be made next? How is the insurance industry collaborating with academia? How is the industry working with insurtech startups? How will these partnerships change in the future?

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Exhibition Area & Refreshment Breaks

End of Summit

03:00 PM 03:05 PM

Thank you for attending! Feel free to continue discussions via the app with your new connections. We look forward to seeing you again soon!