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The adoption of artificial intelligence in the financial service industry, particularly the adoption of machine learning, presents challenges and opportunities. Challenges include algorithmic fairness, explainability, privacy, and requirements of a very high degree of accuracy. For example, there are ethical and regulatory needs to prove that models used for activities such as credit decisioning and lending are fair and unbiased, or that machine reliance doesn’t cause humans to miss critical pieces of data. For some use cases, the operating standards require nothing short of perfect accuracy.
Privacy issues around collection and use of consumer and proprietary data require high levels of scrutiny. Many machine learning models are deemed unusable if they are not supported by appropriate levels of explainability. Some challenges like entity resolution are exacerbated because of scale, highly nuanced data points and missing information. On top of these fundamental requirements, the financial industry is ripe with adversaries who purport fraud and other types of risks.
The aim of this workshop is to bring together researchers and practitioners to discuss challenges for AI in financial services, and the opportunities such challenges represent to the community. The workshop will consist of a series of sessions, including invited talks, panel discussions and short paper presentations, which will showcase ongoing research and novel algorithms.
Fri 5:35 a.m. - 5:50 a.m.
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Opening Remarks
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Talk
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Manuela Veloso · Isabelle Moulinier 🔗 |
Fri 5:50 a.m. - 6:10 a.m.
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Invited Talk 1: Fairness and Causality with Missing Data
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Invited Talk
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Madeleine Udell 🔗 |
Fri 6:10 a.m. - 6:30 a.m.
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Invited Talk 2: Building Augmented Intelligence for a Global Credit Rating Agency
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Invited Talk
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Sameena Shah 🔗 |
Fri 6:30 a.m. - 7:30 a.m.
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Panel: Explainability, Fairness and Human Aspects in Financial Services
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Panel Discussion
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Madeleine Udell · Jiahao Chen · Nitzan Mekel-Bobrov · Manuela Veloso · Jon Kleinberg · Andrea Freeman · Samik Chandarana · Jacob Sisk · Michael McBurnett 🔗 |
Fri 7:30 a.m. - 8:00 a.m.
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Coffee Break and Socialization
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Fri 8:00 a.m. - 8:20 a.m.
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Invited Talk 3: Fairness in Allocation Problems
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Invited Talk
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Michael Kearns 🔗 |
Fri 8:20 a.m. - 9:00 a.m.
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Paper Presentations (see below for paper titles)
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Talks
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11:20 - 11:28 In (Stochastic) Search of a Fairer Alife 11:28 - 11:36 Where's the Bias? Developing Effective Model Goveranance 11:36 - 11:44 Scalable Graph Learning for Anti-Money Laundering: A First Look 11:44 - 11:52 Fair Resource Allocation in a Volatile Marketplace 11:52 - 12:00 Algorithmic Confidence – A Key Criterion for XAI and FAT |
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Fri 9:00 a.m. - 10:30 a.m.
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Lunch
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Fri 10:30 a.m. - 10:50 a.m.
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Invited Talk 4: When Algorithms Trade: Modeling AI in Financial Markets
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Invited Talk
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Michael Wellman 🔗 |
Fri 10:50 a.m. - 11:10 a.m.
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Invited Talk 5: ML-Based Evidence that High Frequency Trading Has Made the Market More Efficient
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Invited Talk
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Tucker Balch 🔗 |
Fri 11:10 a.m. - 11:50 a.m.
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Paper Presentations (see below for paper titles)
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Talks
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02:10 - 02:18: Accurate, Data-Efficient Learning from Noisy, Choice-Based Labels for Inherent Risk Scoring 02:18 - 02:26: An AI-based, Multi-stage detection system of banking botnets 02:26 - 02:34: Robust Classification of Financial Risk 02:34 - 02:42: An Empirical Evaluation of Deep Sequential Models for Volatility Prediction 02:42 - 02:50: Computer-Assisted Fraud Detection, from Active Learning to Reward Maximization |
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Fri 11:50 a.m. - 12:00 p.m.
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Announcement: FICO XAI Challenge Winners
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Announcement
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Fri 12:00 p.m. - 12:30 p.m.
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Coffee Break
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Fri 12:30 p.m. - 12:50 p.m.
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Invited Talk 6: Is it possible to have interpretable models for AI in Finance?
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Invited Talk
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Cynthia Rudin 🔗 |
Fri 12:50 p.m. - 1:30 p.m.
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Paper Presentations (see below for paper titles)
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Talks
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15:50 - 15:58 Use of Machine Learning Techniques to Create a Credit Score Model for Prepaid Basic Services in East Africa. Case Study: Airtime Loans 15:58 - 16:06 Towards Global Explanations for Credit Risk Scoring 16:06 - 16:14 Interpretable Credit Application Predictions With Counterfactual Explanations 16:14 - 16:22 Interpretable Feature Selection Using Local Information for Credit Assessment 16:22 - 16:30 Towards Explainable Deep Learning for Credit Lending: A Case Study |
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Fri 1:30 p.m. - 2:40 p.m.
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Posters and Open Discussions (see below for poster titles)
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Poster Session
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Ramya Malur Srinivasan · Miguel Perez · Yuanyuan Liu · Ben Wood · Dan Philps · Kyle Brown · Daniel Martin · Mykola Pechenizkiy · Luca Costabello · Rongguang Wang · Suproteem Sarkar · Sangwoong Yoon · Zhuoran Xiong · Enguerrand Horel · Zhu (Drew) Zhang · Ulf Johansson · Jonathan Kochems · Gregory Sidier · Prashant Reddy · Lana Cuthbertson · Yvonne Wambui · Christelle Marfaing · Galen Harrison · Irene Unceta Mendieta · Thomas Kehler · Mark Weber · Li Ling · Ceena Modarres · Abhinav Dhall · Arash Nourian · David Byrd · Ajay Chander · Xiao-Yang Liu · Hongyang Yang · Shuang (Sophie) Zhai · Freddy Lecue · Sirui Yao · Rory McGrath · Artur Garcez · Vangelis Bacoyannis · Alexandre Garcia · Lukas Gonon · Mark Ibrahim · Melissa Louie · Omid Ardakanian · Cecilia Sönströd · Kojin Oshiba · Chaofan Chen · Suchen Jin · aldo pareja · Toyo Suzumura
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Fri 2:40 p.m. - 2:45 p.m.
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Closing Remarks
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Talk
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Author Information
Manuela Veloso (JPMorgan and Carnegie Mellon University)
Nathan Kallus (Cornell University)
Sameena Shah (S&P Global)
Sameena is a Managing Director and Director of Research at JP Morgan AI Research. Previously Sameena was a Managing Director and Head of Data Science at S&P Global Ratings. Prior to that she led all AI strategy, Research and development for Financial and News verticals of Thomson Reuters, and worked as a quant at a hedge fund. Sameena has won several best paper awards, has 40+ publications, 11 patents, and has buitl several award winning state of the art AI systems for businesses.
Senthil Kumar (Capital One)
Senthil Kumar is a Director of Data Science at Capital One where he applies Machine Learning and AI to various business problems. Prior to joining Capital One, he was at Bell Labs where he developed and managed several successful products that have been licensed around the world. He has published over 30 papers and holds 6 patents. Most recently, he co-organized the KDD 2017 Workshop on Anomaly Detection in Finance, the 2018 NeurIPS Workshop on Challenges and Opportunities of AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, the 2019 ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, and the 2019 2nd KDD Workshop on Anomaly Detection in Finance.
Isabelle Moulinier (Capital One)
Jiahao Chen (Capital One)

Jiahao Chen is a Vice President and Research Lead at JPMorgan AI Research in New York, with research focusing on explainability and fairness in machine learning, as well as semantic knowledge management. He was previously a Senior Manager of Data Science at Capital One focusing on machine learning research for credit analytics and retail operations. When still in academia, Jiahao was a Research Scientist at MIT CSAIL where he co-founded and led the Julia Lab, focusing on applications of the Julia programming language to data science, scientific computing, and machine learning. Jiahao has organized JuliaCon, the Julia conference, for the years 2014-2016, as well as organized workshops at NeurIPS, SIAM CSE, and the American Chemical Society National Meetings. Jiahao has authored over 120 packages for numerical computation, data science and machine learning for the Julia programming language, in addition to numerous contributions to the base language itself.
John Paisley (Columbia University)
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