Machine Learning Applications for Fraud Detection in Finance Sector

Abstract

AbstractDue to advances in information technology, instantaneous accessibility to financial services through digital channels has increased. Although digital platforms’ usage makes an individual’s life more comfortable, it may also cause some critical consequences like financial fraud which causes critical losses for companies in the industrial sector, investors, and governments. Identification of frauds can be challenging task for a human because it may be necessary to analyse high volume data during long time periods. An alternative is to use financial data as a fraud detection tool to automatically classify fraudulent activities. Currently, there are many practical solutions for automatically detect frauds in the finance domain. In this chapter, we examined on three different fraud types (bank fraud, insurance fraud, and corporate fraud) in finance sector and reviewed the studies using machine learning methods to detect financial fraud in a detailed manner. The findings from this review show that most commonly applied algorithms for financial fraud detection are Decision Tree, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest and most of machine learning-based studies were performed in bank fraud field. This chapter also reveals that deep learning and ensemble-based machine learning applications has been frequently preferred in recent years to improve detection performance of the frauds in finance sector.KeywordsFinancial fraudMachine learningBank fraudInsurance fraudFinancial statement fraudMass marketing fraudSecurities fraudSupervised learningUnsupervised learningEnsemble learningDeep learning