A Machine Learning Framework for Financial Fraud Detection Using Explainable Artificial Intelligence Techniques - NR, Latha; G, Shyamala; GB, Pallavi; Bhat, Sneha Santhosh; Mehrotra, Archit; Seru, Ashish; Gotadke, Tanisha (International Journal of Computer Sciences and Engineering, 2025)
Detection of fraud in business has become increasingly significant with the intricacy and complexity of contemporary fraudulent schemes. This paper presents an exhaustive review of sophisticated approaches integrating machine learning, anomaly detection, and Explainable Artificial Intelligence (XAI) for improving fraud detection systems. Primary preprocessing methods like SMOTE resolve class imbalance, whereas models like Autoencoders and Graph Neural Networks (GNNs) detect anomalous patterns in large and complex datasets efficiently. Classification techniques, like Random Forest and XGBoost, show great performance in detecting fraudulent transactions. Correspondingly, the integration of XAI methods such as SHAP and LIME completes the gap in between accuracy and transparency, finding solutions in order to regulate compliance and attain confidence in automated systems. Recent advances including generative AI models and secure mechanisms have vowed to balance predictive ability and data privacy. Though these developments are underway, scalability, real-time deployment, and expansion to keep up with growing fraud patterns continue to be challenges. This work identifies emerging trends, recognizes key research gaps, and proposes a research plan for creating scalable, interpretable, and adaptive financial fraud detecting systems