Measuring Fairness in Financial Transaction Machine Learning Models

• URL: http://arxiv.org/abs/2501.10784v2
• Date: Wed, 22 Jan 2025 20:34:36 GMT
• Title: Measuring Fairness in Financial Transaction Machine Learning Models
• Authors: Deniz Sezin Ayvaz, Lorenzo Belenguer, Hankun He, Deborah Dormah Kanubala, Mingxu Li, Soung Low, Carlos Mougan, Faithful Chiagoziem Onwuegbuche, Yulu Pi, Natalia Sikora, Dan Tran, Shresth Verma, Hanzhi Wang, Skyler Xie, Adeline Pelletier,
• Abstract summary: Mastercard develops and deploys machine learning models aimed at optimizing card usage and preventing attrition. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency.
• Score: 6.68190250415387
• License:
• Abstract: Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, to tailor bank offerings and maximize revenue opportunities. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency. As part of this effort, Mastercard has sought expertise from the Turing Institute through a Data Study Group to better assess fairness in more complex AI/ML models. The Data Study Group challenge lies in defining, measuring, and mitigating fairness in these predictions, which can be complex due to the various interpretations of fairness, gaps in the research literature, and ML-operations challenges.

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