Explainable AI-Based Hybrid LightGBM–LSTM for Banking Fraud Detection

Abstract

Banking fraud detection faces changing transaction behaviors, class imbalance, and the need to interpret the outputs of complex models. This study develops a Fraud Detection and Analysis System (FDAS) as a Decision Support System in the form of a prototype that integrates tabular and temporal modeling, imbalance handling, operational validation, and post-prediction interpretation. A quantitative experimental approach was employed using a synthetic transaction dataset constructed based on the characteristics of observed fraud and normal patterns, comprising 50 accounts, consisting of 25 fraud accounts and 25 normal accounts, covering the period from January to December 2025. Account-level splitting was used to prevent historical information leakage across accounts, while TPH-SMOTE and KHOI-SMOTE were applied only to the training set. LightGBM and LSTM were trained independently to represent tabular and temporal data, respectively, and their probabilities were subsequently combined through weighted decision-level late-fusion, with SOP serving as the operational validation layer. SHAP was employed as a post-hoc explanation to support interpretability, traceability, and human oversight. Across 1,000 paired evaluation units, the final configuration achieved an Accuracy of 97.4%, Precision of 96.8%, Recall of 96.2%, F1-score of 96.5%, and ROC-AUC of 0.984. Comparison across configurations indicated that the highest performance was achieved by the integrated configuration within the experimental environment used. FDAS demonstrates end-to-end integration to support fraud investigation through recommendations and interpretive information, while the final decision remains with the analyst. The use of synthetic data, 50 accounts, and a prototype status limits direct generalization to operational banking populations and production readiness.

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