Applications of Machine Learning and Deep Learning Algorithms in Financial Fraud Detection: A Review

Financial fraud detection is a critical task in the modern financial landscape, where fraudulent activities like credit card fraud, online payment fraud, bank fraud, and insurance fraud pose significant challenges to the security and integrity of financial transactions. Traditional fraud detection systems often fall behind the ever-changing tactics used by fraudsters. However, recent advancements in machine learning (ML), as well as deep learning (DL) techniques, offer promising solutions to detect and prevent fraudulent activities with higher accuracy and efficiency. ML and DL methods use algorithms and models to evaluate vast amounts of transactional data, identify patterns, and detect abnormalities that indicate fraudulent activity. This paper provides a comprehensive overview of the application of ML and DL techniques in financial fraud detection, highlighting their capabilities, limitations, and future directions. In the digital age, financial institutions can improve their fraud detection capabilities, manage risks, and protect stakeholders’ interests by implementing modern technologies and innovative methods. According to the studies evaluated, the most common type of fraud identified by XAI methods is credit card fraud, and XAI and metaheuristic-based AI are effective fraud detection algorithms.