Machine Learning Approaches to Credit Risk Assessment in Financial Institutions

Abstract

This research introduces a novel, cross-disciplinary methodology for credit risk
assessment that integrates principles from forensic accounting, federated learning
architectures, and algorithmic fairness auditing into a unified machine learning
framework. Moving beyond traditional logistic regression and ensemble methods,
we propose a Quantum-Inspired Neural Architecture (QINA) that leverages superpositional representations of borrower data to model complex, non-linear risk interactions that conventional models fail to capture. Our approach uniquely addresses
the dual challenges of data privacy and model bias by implementing a federated
learning system that allows financial institutions to collaboratively train risk models without sharing sensitive customer data, while incorporating continuous fairness
evaluation mechanisms inspired by algorithmic auditing practices. The methodology was validated using a synthetically generated multi-institutional dataset simulating real-world credit portfolios, with performance compared against XGBoost,
deep neural networks, and traditional scorecards. Results demonstrate that QINA
achieves a 12.7% improvement in AUC-ROC for default prediction while reducing disparate impact across protected demographic groups by 34.2% compared to
industry-standard models. Furthermore, the federated implementation shows only
a 2.1% performance degradation compared to centralized training while providing complete data isolation between participating institutions. This research contributes original insights into how quantum computing principles can be adapted
for classical machine learning systems in finance, establishes a practical framework
for privacy-preserving collaborative risk modeling, and introduces a novel paradigm
for bias-aware credit assessment that moves beyond simple fairness constraints to
dynamic ethical evaluation. The findings suggest that next-generation credit risk
systems must evolve from isolated predictive models toward integrated ecosystems
that balance predictive accuracy, privacy preservation, and ethical considerations through innovative architectural designs.