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Workshop
Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
Manuela Veloso · Nathan Kallus · Sameena Shah · Senthil Kumar · Isabelle Moulinier · Jiahao Chen · John Paisley

Fri Dec 07 05:00 AM -- 03:30 PM (PST) @ Room 511 CF

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.

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

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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