Measuring Fairness in Financial Transaction Machine Learning Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913661652566016 |
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| author | Ayvaz, Deniz Sezin Belenguer, Lorenzo He, Hankun Kanubala, Deborah Dormah Li, Mingxu Low, Soung Mougan, Carlos Onwuegbuche, Faithful Chiagoziem Pi, Yulu Sikora, Natalia Tran, Dan Verma, Shresth Wang, Hanzhi Xie, Skyler Pelletier, Adeline |
| author_facet | Ayvaz, Deniz Sezin Belenguer, Lorenzo He, Hankun Kanubala, Deborah Dormah Li, Mingxu Low, Soung Mougan, Carlos Onwuegbuche, Faithful Chiagoziem Pi, Yulu Sikora, Natalia Tran, Dan Verma, Shresth Wang, Hanzhi Xie, Skyler Pelletier, Adeline |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measuring Fairness in Financial Transaction Machine Learning Models Ayvaz, Deniz Sezin Belenguer, Lorenzo He, Hankun Kanubala, Deborah Dormah Li, Mingxu Low, Soung Mougan, Carlos Onwuegbuche, Faithful Chiagoziem Pi, Yulu Sikora, Natalia Tran, Dan Verma, Shresth Wang, Hanzhi Xie, Skyler Pelletier, Adeline Machine Learning 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. |
| title | Measuring Fairness in Financial Transaction Machine Learning Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.10784 |