RABEM: risk-adaptive Bayesian ensemble model for fraud detection.

The digital transaction ecosystem presents a critical problem involving financial fraud detection and, on the verge, requires advanced computational techniques to distinguish between legitimate and fraudulent activities. To close the gap in available robust fraud detection methodologies, this study utilizes the Synthetic Financial Datasets provided by Kaggle, a collection of synthetic 6 million transactions that include a rich data benchmark generated by PaySim's top-notch synthetic data generation process. It presents the Risk Adaptive Bayesian Ensemble Model (RABEM), a new system that combines various advanced methods, including Black-Scholes Feature Engineering, Hybrid VAE, Nyström Approximation Gaussian Process, Random Projection Tree (RPTree), and Gated Recurrent Unit (GRU) and Bayesian Reliability Fusion to provide improved accuracy and dependability of fraud detection. Furthermore, the RABEM methodology proposed is demonstrated to deliver excellent performance on various evaluation metrics, achieving a high accuracy of 99.38%, which outperforms other approaches. The Matthews Correlation Coefficient (MCC) value of 0.9788, low Brier Score of 0.0061, and log loss of 0.2103 are key performance indicators. The top-K hit rate analysis demonstrates the model's reasonable ability to identify fraud, as it correctly identifies 972 out of 1000 fraudulent transactions with a precision of 0.972. Future work will focus on working with a large set of related data and all other ensemble methods, and creating more effective strategies for selecting essential features to improve fraud detection accuracy and speed in complex financial transactions.