Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review

TLDR

Financial fraud is a growing menace, and conventional manual methods are imprecise, costly, and time‑consuming, prompting the use of AI‑driven machine‑learning techniques to analyze large volumes of financial data for fraud detection. This systematic literature review aims to synthesize and evaluate existing research on machine‑learning approaches for detecting financial fraud. Using the Kitchenham protocol, the authors searched major electronic databases, applied inclusion/exclusion criteria to select 93 studies, and extracted information on popular ML algorithms, fraud types, and evaluation metrics. The review found that support vector machines and artificial neural networks are the most frequently used algorithms, credit‑card fraud is the predominant fraud type studied, and it highlights gaps, limitations, and future research directions.

Abstract

Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.