Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree

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Author(s): Ntebogang Dinah Moroke (North West University, South Africa)and Katleho Makatjane (Department of Statistics, University of Botswana, Botswana)
Copyright: 2022
Pages: 21
Source title: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
Source Author(s)/Editor(s): Victor Lobo (NOVA Information Management School (NOVA-IMS), NOVA University Lisbon, Portugal & Portuguese Naval Academy, Portugal)and Anacleto Correia (CINAV, Portuguese Naval Academy, Portugal)
DOI: 10.4018/978-1-7998-9430-8.ch002

Keywords: Cyber and Network Security / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Financial fraud remains one of the most discussed topics in literature. The financial scandals of Enron, WorldCom, Qwest, Global Crossing, and Tyco resulted in approximately 460 billion dollars of loss. The detection of financial fraud, therefore, has become a critical task for financial practitioners. Three factors determine the likelihood of fraud occurrence, including pressure, opportunity, and rationalization. The core of these factors lies in people's beliefs and behaviour. Due to the unpredictability and uncertainty in fraudsters' incentives and techniques, fraud detection requires a skill set that encompasses both diligence and judgment. Big data technologies have had a huge impact on a wide variety of industries because they tend to be ubiquitous, starting in the last decade and continuing today.

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Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree

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