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JCSIS

Explainable AI-Driven Fraud Detection System for Real-Time Transaction Monitoring in Digital Payment Platforms.

Hakan Khan
Volume 5

Abstract

Digital payment platforms process massive volumes of transactions in real time, creating significant challenges for fraud detection systems due to the high velocity of data streams, evolving fraud strategies, severe class imbalance, and the need for immediate and interpretable decision-making. Traditional rule-based systems and conventional machine learning models often fail to adapt to emerging fraudulent behaviors and may generate excessive false alarms, while complex deep learning models frequently operate as black boxes, limiting their acceptance in regulated financial environments. This paper proposes an explainable artificial intelligence-driven fraud detection system for real-time transaction monitoring in digital payment platforms. The proposed framework integrates advanced machine learning and deep learning models, including XGBoost, LightGBM, Random Forest, Autoencoders, Long Short-Term Memory networks, and Graph Neural Networks, to detect suspicious transaction patterns across heterogeneous payment data. The system analyzes multiple transaction-level and user-level features, including transaction amount, frequency, merchant category, device fingerprint, geolocation, payment channel, transaction time, user behavioral history, account age, velocity indicators, and network relationships among accounts, merchants, and devices. A comprehensive preprocessing pipeline was applied, including missing value handling, categorical encoding, feature normalization, temporal aggregation, anomaly feature construction, and imbalance-aware learning using SMOTE, focal loss, and cost-sensitive classification. To support real-time deployment, the framework incorporates streaming-based inference, low-latency feature extraction, threshold optimization, and adaptive risk scoring, enabling immediate classification of transactions as legitimate, suspicious, or fraudulent. Explainability was achieved using SHAP, LIME, attention-based visualization, and rule extraction techniques to identify the most influential fraud indicators behind each prediction, allowing analysts to understand model decisions and investigate suspicious activities more effectively. Experimental results demonstrate that the proposed explainable fraud detection system achieves superior performance compared with traditional rule-based engines, logistic regression, Support Vector Machine, standalone Random Forest, and conventional neural network baselines. The proposed system records higher accuracy, precision, recall, F1-score, AUC-ROC, and precision-recall AUC, while significantly reducing false positives and improving detection of rare fraudulent transactions. Furthermore, interpretability analysis reveals that abnormal transaction velocity, unusual geolocation, high-risk merchant categories, device switching, atypical transaction amounts, and irregular account behavior are among the most important predictors of fraud. The proposed framework provides a scalable, adaptive, and transparent solution for real-time fraud monitoring, offering practical value for banks, fintech companies, payment gateways, and regulatory bodies seeking to improve transaction security, reduce financial losses, and strengthen trust in digital payment ecosystems.

Keywords

References

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