Measuring Fairness in Financial Transaction Machine Learning Models

TL;DR

This paper discusses the challenge of defining, measuring, and mitigating fairness in machine learning models used for financial transactions, emphasizing Mastercard's efforts to evaluate fairness within their AI governance framework.

Contribution

It introduces a framework for assessing fairness in complex financial ML models and highlights Mastercard's collaboration with the Turing Institute to address these challenges.

Findings

Identified key challenges in measuring fairness in financial ML models

Proposed approaches for fairness evaluation in complex AI systems

Emphasized the importance of transparency and efficacy in AI governance

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.