Applied IT & Engineering
Information and engineering sciences | Online ISSN 3068-0115
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Volume 2 Number 1 2024
RESEARCH ARTICLE (Open Access)
Development of a Scalable Bias Mitigation Framework for AI-Based Consumer Credit Scoring in Regulated U.S. Lending Models
Sonia Nashid1*, Md Nazmuddin Moin Khan2
Applied IT & Engineering 2 (1) 1-9 https://doi.org/10.25163/engineering.2110481
Submitted: 09 August 2024 Revised: 02 October 2024 Accepted: 10 October 2024 Published: 12 October 2024
Abstract
Background: The U.S. lending industry operates with AI-based consumer credit scoring systems but these systems maintain existing prejudices which stem from personal characteristics and monetary situations. The creation of bias reduction methods which work on a large scale stands as a fundamental requirement to protect fairness while keeping prediction accuracy intact.
Methods: The research gathered data from 295 participants who provided their demographic information through age and gender and education level and occupation and marital status and their financial behavior through debt-to-income ratio and repayment timeliness and savings and loan approval outcomes. Pearson correlation analysis examined relationships among variables, and association analysis using standardized Beta coefficients, p-values, and chi-square (χ²) statistics assessed predictor influence on lending outcomes.
Results: Age showed positive correlations with education (r = 0.38, p = 0.01) and repayment timeliness (r = 0.30, p = 0.01). The analysis shows that education and savings function as main predictors but gender and marital status do not show significant effects after mitigation. The AI model showed a decrease in performance metrics with accuracy dropping from 82.1 to 80.5 and precision from 78.3 to 77.9 and recall from 75.6 to 75.0 but the fairness metrics demonstrated substantial improvement through decreased demographic parity and equal opportunity differences.
Conclusion: The bias mitigation framework achieves both fairness improvement and predictive reliability maintenance for AI-based credit scoring systems. The mixture of demographic data with financial minutiae and interactive patterns allows fair and see-through loaning selections under U.S. regulatory values.
Keywords: Financial behavior, bias mitigation, AI-based credit scoring, lending fairness, predictive modeling
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