Machine Learning for Financial Fraud Detection: A Survey of Algorithms and Applications

Abstract

Financial fraud has emerged as a serious challenge faced by financial institutions as a result of the rise of online banking, electronic payment systems, and electronic transactions. One of the main problems faced by advanced fraud detection solutions is that they fall behind in dealing with evolving and complicated methods of fraud. Modern fraud detection techniques make use of machine learning (ML) which allows for processing large volumes of financial data and detecting anomalies in the data, as well as suspicious trends and possible fraud. This research paper discusses the methods of financial fraud detection by means of ML in detail. It features several forms of financial frauds including credit card fraud, insurance fraud, fraud in financial statements, and cyber fraud. The research paper reviews different algorithms including Logistic Regression, Random Forest, Support Vector Machine, K-Means, Isolation Forest, and Long Short-Term Memory. It provides detailed information about supervised and unsupervised learning approaches. In addition to that, the paper discusses challenges faced by the automation of fraud detection, including unbalanced datasets, changing trends of fraud, lack of labeled data, false positives, difficulty of understanding the models, and computing power.