Credit risk prediction is one of the most consequential applications of quantitative modelling in the banking and financial sector, sitting alongside market-risk pricing models, time-series volatility forecasts, and portfolio-optimization frameworks as part of the mathematical and statistical backbone that underpins modern banking systems. This paper first reviews that broader backbone — the Black-Scholes-Merton option-pricing framework, ARIMA and GARCH time-series models used for volatility and interest-rate forecasting, and the Markowitz mean-variance framework for portfolio risk — and critically discusses the assumptions and limitations of each in today's volatile, structurally-shifting economic environment. Building on this foundation, the paper narrows its focus to credit risk and presents an original empirical study that classifies loan applicants as low-risk or high-risk. A real-world dataset containing 32,581 loan records with 12 borrower attributes was preprocessed by imputing missing values, encoding categorical variables, and normalizing skewed numerical features. Four supervised machine learning algorithms — Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting — were trained on an 80/20 split and evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Random Forest achieved the strongest overall performance (93.36% accuracy, 97.14% precision, 82.48% F1-score, ROC-AUC = 0.9313), followed closely by Gradient Boosting (92.70% accuracy, ROC-AUC = 0.926), while both ensemble methods clearly outperformed the linear Logistic Regression baseline (85.12% accuracy). The results demonstrate that tree-based ensemble learning provides materially more reliable credit risk classification than either classical linear scoring or single decision trees, and the paper closes by situating these findings against the well-documented fragility of constant-parameter statistical models such as Black-Scholes, ARIMA/GARCH, and Markowitz mean-variance optimization under regime shifts and market stress.
Chandel et al. (Fri,) studied this question.