Credit card fraud is a nagging problem in the world of credit transactions, which significantly leads to massive economic losses, and undermines users' confidence. Conventional fraud detection mechanisms are typically not adaptive, nor interpretable, thus being unsuitable for emerging fraud patterns and financial environments driven by compliance. In this paper, we introduce a smart and explainable credit card fraud detection system, with “smart” being a keyword to indicate an adaptive, modular, and tunable model architecture specialized for imbalanced data, and “explainable” for providing a transparent and feature-level explanation for any decision made by the model, utilizing the SHAP (SHapley Additive exPlanations) technique. The model we implemented is composed of these two libraries: the method decides to use XGBoost as a classifier and takes Random Forest as a benchmark. The two models are trained and evaluated for performance on the imbalanced Kaggle Credit Card Fraud Detection dataset, using stratified 5-fold cross-validation and grid search for hyperparameter selection. The final XGBoost model is better able to distinguish between classes, with 92.1% precision and 87.3% recall. SHAP is integrated into the prediction pipeline as a means of creating instance-level explanations to achieve post hoc analysis and meet GDPR and PCI DSS compliance. These interpretations and predictions are supplied and protected via role-based access control and encryption for audit. Experimental results show the model’s power to accurately detect rare fraud examples in a transparent and operationally robust way. This work addresses the trade-off between prediction performance and interpretability, and enables safe, real-time fraud detection in contemporary financial institutions. It also provides a deployable design that satisfies regulatory requirements and an effective analyst workflow, making it applicable for a production-based financial security system.

Credit Card Fraud XGBoost Explainable AI SHAP Machine Learning Fraud Detection Imbalanced Dataset Model Interpretability Adaptive Learning Financial Security