Advanced Explainable Hybrid Metaheuristic–Deep Learning Framework for Real-Time Financial Fraud Detection with Temporal Convolutional Analysis

The increasing digitization in banking and related financial services has resulted in spurring the level of transactions with fraudulent patterns and thus demands detection solutions not only efficient but also interpretable and replicable. The earlier machine learning approaches, like K-Nearest Neighbors, Decision Trees, and Random Forests, are not efficient in dealing with high-dimensional and sequential patterns in transactions; in addition, they are incapable of modeling time patterns and are not interpretable models. Since there exist drawbacks in earlier approaches, this work introduces an Interpretable Moth-Flame Optimized Temporal Convolutional Network (MFO-TCN) for efficient and interpretable real-time financial fraud detection. The approach is initiated with rigorous data preprocessing tasks like normalization and encoding performed on the Bank Account Fraud (BAF) dataset. Based on the Moth-Flame Optimization (MFO) algorithm, the optimal features of the transactions expressing high discriminative powers are extracted. This is followed by the application of the Temporal Convolutional Network (TCN) technique, which is capable of identifying the sequential patterns of fraud-related activities. For improved transparency and validity, the SHAP explainability technique has been adopted, ensuring better explanations for feature importance and decision-making. The proposed MFO-TCN results in an accuracy of 97.2% with higher values of precision and recall, achieving better results in comparison to classical and ensemble approaches. Moreover, it provides real-time processing for transactions in milliseconds. The above results show that an efficient combination of metaheuristics for feature optimization, along with temporal deep networks, can provide an optimal technique for financial fraud detection systems.