Machine Learning Approaches to Credit Risk Assessment in Financial Institutions (gjstudies, 2021)
This research introduces a novel, cross-disciplinary methodology for credit riskassessment that integrates principles from forensic accounting, federated learningarchitectures, and algorithmic fairness auditing into a unified machine learningframework. 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 addressesthe dual challenges of data privacy and model bias by implementing a federatedlearning system that allows financial institutions to collaboratively train risk models without sharing sensitive customer data, while incorporating continuous fairnessevaluation 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 QINAachieves a 12.7% improvement in AUC-ROC for default prediction while reducing disparate impact across protected demographic groups by 34.2% compared toindustry-standard models. Furthermore, the federated implementation shows onlya 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 adaptedfor classical machine learning systems in finance, establishes a practical frameworkfor privacy-preserving collaborative risk modeling, and introduces a novel paradigmfor bias-aware credit assessment that moves beyond simple fairness constraints todynamic ethical evaluation. The findings suggest that next-generation credit risksystems must evolve from isolated predictive models toward integrated ecosystemsthat balance predictive accuracy, privacy preservation, and ethical considerations through innovative architectural designs.