Transaction Behavior Analysis for Early Fraud Detection Using Interpretable Models

Detecting irregularities in financial transactions has become easier with the development of machine learning (ML) tools. The usefulness of machine learning algorithms in detecting credit card fraud is investigated in this paper, which also highlights important developments in the field. A significant worry now is credit card fraud as the financial sector embraces more digital transactions. To ensure fair and unbiased decision-making, ethical issues with algorithmic bias, data privacy, and transparency must still be addressed. This study attempts to provide an approach to fraud detection that use interpretable models to analyze transaction behavior patterns in order to identify fraud early through the use of SHAP-explained Gradient Boosting, Decision Trees, and Logistic Regression. As a result, the goal of the study is to determine how well interpretable machine learning models can detect fraud in transactional datasets. to ascertain efficient methods for maximizing the trade-off between Explainability and model performance. Results from the study showed that the logistic regression model produced good results on the five-fold cross validation, with an accuracy of 99.90%, precision of 100%, recall of 90%, and F1-score of 93.3%. In order to meet the operational needs of fraud detection systems in real-time financial contexts, the study’s technique enhanced explainability and efficiency.