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The effects of data preprocessing on probability of default model fairness

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  • Di Wu

Abstract

In the context of financial credit risk evaluation, the fairness of machine learning models has become a critical concern, especially given the potential for biased predictions that disproportionately affect certain demographic groups. This study investigates the impact of data preprocessing, with a specific focus on Truncated Singular Value Decomposition (SVD), on the fairness and performance of probability of default models. Using a comprehensive dataset sourced from Kaggle, various preprocessing techniques, including SVD, were applied to assess their effect on model accuracy, discriminatory power, and fairness.

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  • Di Wu, 2024. "The effects of data preprocessing on probability of default model fairness," Papers 2408.15452, arXiv.org.
    • Handle: RePEc:arx:papers:2408.15452
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    1. Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020. "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 91-95, May.
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