Enhancing Banking Fraud Detection: Role of Machine Learning and Deep Learning Methods

Fraud detection in banking is a critical concern as financial institutions face increasing challenges in identifying and preventing fraudulent activities. With the rise of sophisticated fraud schemes, traditional detection methods have proven inadequate, prompting the adoption of machine learning (ML) and deep learning (DL) techniques. This review explores the application of ML and DL methods in banking fraud detection, examining their strengths, limitations, and potential for future improvements. We provide an overview of commonly used ML algorithms such as logistic regression, decision trees, random forests, and support vector machines, as well as advanced DL architectures, including feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). In this review, key issues are discussed in data preprocessing, such as handling imbalanced datasets, feature engineering, and ensuring data privacy. Emerging trends in fraud detection, including explainable AI, real-time and edge computing solutions, blockchain integration, and synthetic data generation, are highlighted as promising avenues for enhancing detection systems. Despite the significant progress, challenges such as computational complexity, model interpretability, and adversarial attacks remain. This review concludes by emphasizing the need for continued research and collaboration between academia and industry to develop more effective, transparent, and secure fraud detection systems for the banking sector.