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

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

Ntebogang Dinah Moroke, Katleho Makatjane
ISBN13: 9781799894308|ISBN10: 1799894304|ISBN13 Softcover: 9781799894315|EISBN13: 9781799894322
DOI: 10.4018/978-1-7998-9430-8.ch002
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MLA

Moroke, Ntebogang Dinah, and Katleho Makatjane. "Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree." Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity, edited by Victor Lobo and Anacleto Correia, IGI Global, 2022, pp. 25-45. https://doi.org/10.4018/978-1-7998-9430-8.ch002

APA

Moroke, N. D. & Makatjane, K. (2022). Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree. In V. Lobo & A. Correia (Eds.), Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity (pp. 25-45). IGI Global. https://doi.org/10.4018/978-1-7998-9430-8.ch002

Chicago

Moroke, Ntebogang Dinah, and Katleho Makatjane. "Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree." In Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity, edited by Victor Lobo and Anacleto Correia, 25-45. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9430-8.ch002

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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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