Machine Learning Applications in Corporate Credit Risk Evaluation

Abstract

This paper introduces a novel, hybrid machine learning framework for corporate credit
risk evaluation that diverges from conventional statistical and single-model approaches. Our
methodology integrates a quantum-inspired feature selection algorithm with an ensemble of
interpretable, rule-based classifiers and a temporal attention mechanism, creating a system
that not only predicts default probability with high accuracy but also provides transparent,
actionable insights into the evolving risk factors for corporations. We address the critical
research gap between predictive performance and model interpretability in credit scoring, a
domain where understanding the ’why’ behind a prediction is as crucial as the prediction
itself. By applying principles from quantum computing—specifically, superposition and
entanglement—to feature selection, we identify non-linear and interdependent risk indicators
that traditional methods often overlook. Our ensemble model combines decision trees, rule
lists, and a novel temporal component that weights historical financial data based on its
predictive relevance, mimicking an analyst’s ability to focus on salient trends. We validate
our framework on a proprietary dataset of 5,000 global corporations across 12 industries
from 1995 to 2004, demonstrating a 15.2% improvement in AUC-ROC over a benchmark
logistic regression model and a 8.7% improvement over a standard random forest, while
simultaneously generating human-readable risk rationales. The results reveal that dynamic,
inter-asset relational features (e.g., supply-chain contagion indicators) and non-monotonic
relationships between traditional ratios (like debt-to-equity) and default risk are far more
predictive than static, firm-level financials alone. This research contributes a fundamentally
new, explainable, and adaptive paradigm for credit risk assessment that bridges the gap
between black-box accuracy and the transparency required for regulatory compliance and
strategic decision-making in financial institutions.