Measuring Fairness in Financial Transaction Machine Learning Models
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.
Cite
@article{arxiv.2501.10784,
title = {Measuring Fairness in Financial Transaction Machine Learning Models},
author = {Deniz Sezin Ayvaz and Lorenzo Belenguer and Hankun He and Deborah Dormah Kanubala and Mingxu Li and Soung Low and Carlos Mougan and Faithful Chiagoziem Onwuegbuche and Yulu Pi and Natalia Sikora and Dan Tran and Shresth Verma and Hanzhi Wang and Skyler Xie and Adeline Pelletier},
journal= {arXiv preprint arXiv:2501.10784},
year = {2025}
}
Comments
Mastercard Data Study Group Alan Turing Institute: https://www.turing.ac.uk/news/publications/data-study-group-final-report-mastercard