An Ensemble-based Fraud Detection Model for Financial Transaction Cyber Threat Classification and Countermeasures

Abstract

Fraud remains a pervasive challenge within the banking industry, where financial institutions and their clients grapple with substantial annual losses. The proliferation of digital transactions and online banking has created new avenues for fraudsters to exploit vulnerabilities, leading to financial harm to unsuspecting victims. Consequently, the imperative to promptly and accurately detect fraudulent transactions has grown significantly, both as a safeguard against financial crimes and as a pillar of trust between customers and the banking sector. This paper introduces an innovative fraud detection model designed for bank payment transactions using advanced ensembling techniques. This study presents a comprehensive evaluation of an ensembling model conducted on the Bank Account Fraud (BAF) dataset. Through meticulous analysis, the performance of various base models and ensembling methods was assessed and compared, employing a variety of critical metrics including accuracy, precision, recall, and F1-score. The proposed ensemble model, referred to as "Stacking," exhibited remarkable performance, attaining a commendable accuracy score of 0.98. This result reaffirmed its prowess as a comprehensive and balanced solution to the multifaceted challenges of fraud detection. This study has paramount implications for the banking industry, offering a robust and adaptable solution to deal with the increasing threats posed by financial fraud. Furthermore, it emphasizes the significance of precision-recall trade-offs in fraud detection and underscores the potential of ensemble methods, particularly the "Stacking" model, to fortify the resilience and efficacy of existing security systems.