Explainable AI (XAI) Transparency for Financial Fraud Detection

With machine learning pipelines becoming significant in the flagging of abnormal card payment patterns, financial institutions are facing the challenge of improved regulatory compliance and end-user trust due to ambiguous “black-box” decisions. The study looks at modern Explainable AI (XAI) equipment that could make fraud-detection models transparent without sacrificing accuracy using the publicly available Credit Card Fraud Detection Dataset 2023 [30]. The first stage is an unsupervised anomaly detector (deep-autoencoder) that enables the full unlabeled stream of suspicious transactions. Itemss to be filtered, it dramatically reduces the amount presented to analysts. The filtered data by a four representative supervised learners are classified as follows: Logistic Regression, Decision Tree, Random Forest, and XGBoost with an interpretable glass-box baseline (Explainable Boosting Machine, EBM) and an LSTM for sequence-aware deep learning. We generated global and local SHAP and LIME explanations of the model, whereas EBM intrinsically exposes additive feature curves. Explanations were compared and benchmarked on fidelity, stability, and cognitive load within a simulated analyst dashboard environment. XGBoost proved to be the best performer in detection by all means (AUC almost 0.995; F1≈0.91 criterion) but required post-hoc SHAP elucidation on non-linear interaction effects of transactional amount and PCA variables from the model perspective. Even the EBM lagged slightly behind for accuracy (AUC ≈0.965) but endowed transparency for effortless auditing of feature effects and interaction terms on the spot. Explanation scores led to a 38% increase in investigation speed in the user study, whilst ranked SHAP feature attributions caused a 12% decrease in false-positive escalations. Coupling unsupervised screening with supervised classifiers enhanced by model-agnostic XAI provides both high recall and actionable insights to satisfy regulatory “right-to-explanation” demands. In conclusion, provided is a realistic template for production environments that will be equipped with trustworthy fraud-monitoring systems, real-time principles, and guidelines.