AI-Driven Credit Scoring Models: Fairness, Accuracy, and Regulatory Risk in Lending Markets - Odunayo Oyasiji; Adeola Okesiji; Chikaome Chimara Imediegwu; Okeoghene Elebe; Opeyemi Morenike Filani (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This examines the growing use of AI-driven credit scoring models in lending markets, with particular focus on the critical dimensions of fairness, accuracy, and regulatory risk. As financial institutions increasingly adopt machine learning (ML) and artificial intelligence (AI) tools to assess creditworthiness, these models offer significant advantages over traditional credit scoring methods by processing vast datasets and uncovering complex, non-linear relationships in borrower behavior. However, alongside these benefits, AI-based credit scoring introduces new ethical and regulatory challenges that demand urgent attention.A central concern is algorithmic fairness. AI models trained on historical credit data may inherit and even amplify existing societal biases, leading to discriminatory lending outcomes. This highlights common sources of bias, including biased data collection, feature selection, and algorithmic design, and discusses mitigation strategies such as fairness-aware learning algorithms and post-hoc adjustments. Techniques like demographic parity and equalized odds are explored as potential fairness metrics to ensure equitable lending outcomes.Additionally, this investigates the predictive accuracy of AI models compared to traditional credit scoring techniques. While AI models generally outperform conventional methods in terms of precision and recall, issues such as overfitting, model drift, and lack of interpretability pose significant challenges for long-term model reliability.The research also addresses regulatory risks, focusing on compliance with laws such as the Equal Credit Opportunity Act (ECOA), General Data Protection Regulation (GDPR), and emerging AI regulations. Emphasis is placed on the need for model explainability, transparency, and robust auditing mechanisms to satisfy legal standards and build consumer trust.Ultimately, this calls for multi-stakeholder collaboration among regulators, financial institutions, technologists, and consumer advocates to develop balanced, ethical, and transparent AI-driven credit scoring systems that enhance financial inclusion while mitigating systemic risks.