Machine Learning Models for Credit Risk Prediction and Financial Fraud Detection

Abstract

Credit risk prediction and financial fraud detection are two of the most important applications of machine learning in finance. Both require classifying cases, borrowers or transactions, as risky or safe. Yet they differ in important ways. Credit default is relatively common, and its prediction must be stable and explainable. Fraud is rare, adversarial and fast-changing, and must be detected in real time. This article reviews and compares the main machine-learning models for these tasks: logistic regression, decision trees, random forests, gradient boosting, neural networks and unsupervised anomaly detection. It also examines how data preparation, class imbalance and evaluation metrics shape model performance. Two simulation experiments illustrate key lessons. In a credit-risk scenario where risk factors acted mostly additively, logistic regression performed as well as or better than machine-learning models, with an AUC of 0.750 against 0.745 for random forest. In a scenario with strong interactions and threshold effects, random forest and gradient boosting achieved AUCs of 0.822 and 0.817, far above logistic regression’s 0.669. In a fraud scenario with a fraud rate of 0.36 percent, random forest achieved the highest precision–recall AUC (0.196). It detected 49.5 percent of frauds when 1 percent of legitimate transactions were flagged, compared with 23.9 percent for an unsupervised isolation forest. The Indian context is described, including a rise in frauds reported by banks from 13,564 cases in 2022–23 to 36,075 in 2023–24. The article concludes that model choice should be guided by the structure of the data, appropriate metrics and the need for explainability and fairness.