Prediction of bank transaction fraud using TabNet an adaptive deep learning architecture

2026-07-20

AI Summary

This study addresses the critical need for high accuracy, interpretability, and scalability in online banking fraud detection by leveraging real-world transaction data from India. Through exploratory data analysis and SMOTE-based oversampling to mitigate class imbalance, the authors systematically evaluate five deep learning models: DNN, GRU, LSTM, 1D-CNN, and TabNet. Notably, they harness TabNet’s intrinsic sparse feature selection mechanism to simultaneously enhance model interpretability and generalization. Experimental results demonstrate that TabNet achieves a 97.39% accuracy and a 0.9739 ROC-AUC under three-fold cross-validation, significantly outperforming baseline models. The approach effectively reduces both false positives and false negatives, supports real-time deployment, and satisfies stringent financial regulatory requirements for model transparency.

Abstract

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal. We utilize a supervised learning pipeline that incorporates the Synthetic Minority Oversampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives. Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

Problem

• fraud detection
• online banking
• transaction anomaly
• regulatory compliance
• interpretable AI

Innovation

• TabNet
• fraud detection
• explainable AI
• SMOTE
• tabular data