Machine Learning‑Based Discriminant Analysis of Corporate Credit Default

Credit Risk Assessment and Explainability of Machine Learning

The credit risk model should choose between forecasting ability, interpretation requirements, misjudgment cost, and compliance conditions, and form a more stable application path through traditional model benchmarks, machine learning assistance, interpretation tools, and manual review.

Construction of SME Loan Default Risk Prediction System for Commercial Banks Using XGBoost Model

Small and medium-sized enterprises play an important role in promoting employment and innovation, but their small scale, opaque financial information, and unstable operating conditions increase credit risk for commercial banks. Accurate prediction of SME loan default risk can reduce credit losses, optimize credit-resou...

Explainable Machine Learning for Home Equity Line of Credit Risk Assessment: A Multi-Model Comparison with SHAP Interpretability

The findings indicate that interpretable machine learning can give lenders a defensible account of every credit decision — a capability that serves both regulatory oversight and the individuals whose applications are under review.

Predicting Credit Risk with ESG Factors Using XGBoost and Structural Learning in Vague Environments (SLAVE) in Commercial Banks

Regression results reveal that profitability and ESG score significantly reduce credit risk and liquidity risk and monetary freedom increase credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.

Machine Learning Applications in Credit Risk Prediction

Machine Learning in Small Business Finance: SBA Loan Default Prediction with XGBoost and Explainable AI

This study advances small business credit risk assessment by comparing traditional statistical models with advanced machine learning (ML) techniques, using a publicly available data set of 89,621 U.S. Small Business Administration (SBA)-backed loans. The analysis compares logistic regression, decision tree and extreme...