The effects of data preprocessing on probability of default model fairness

Di Wu

Papers from arXiv.org

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.

Date: 2024-08
New Economics Papers: this item is included in nep-ban, nep-big, nep-ipr and nep-rmg
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Citations: View citations in EconPapers (1)

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