Volume 6 • Issue 2 • PP: 09–16 • 2026
Disparate Impact in FinTech Credit Scoring: A Multi-Group Fairness Audit with Mitigation Analysis
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Machine learning credit scoring There is no intentional discrimination in models. They differentiate according to data. Such models, trained on borrower histories binned by income, are known as stratified models. absorb and convert the economic situation of the disadvantaged groups and convert them into differential approval rates that last and does not depend on actual creditworthiness. This paper makes a systematic fairness An analysis of three popular classifiers (logistic regression, On a dataset of digital lending, random forest, and XGBoost. Calibrated against statistics of the US consumer credit market. Deploying four Specific fairness measures for each income, gender and age group, and we observe that there is statistically and economically a disparate impact based on income. The poorest fifth of the population earns. approval rates a whopping two decades lower than the top 20.approval scores 20 percentage points lower than the highest. It is not the case that either quintile or logistic regression are 4/5 adverse.Neither quintile nor logistic regression are 4/5 adverse. The standard decision threshold was used and the impact rule was applied. Neither income reweighting nor threshold calibration can get rid of the bias fully since it doesn’t address the bias directly. sacrificing predictive performance. Threshold calibration alone can get into an approximate parity of approval, but with a price of differential. Error rates that present lenders with equal opportunity issues. The results have direct implications for the deployment of The application of algorithmic credit scoring in new regulation regimes, and This contains the EU Artificial Intelligence Act and the US fair lending law.
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