Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

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Author(s): Md Ariful Haque (Old Dominion University, USA)and Sachin Shetty (Old Dominion University, USA)
Copyright: 2022
Pages: 30
Source title: Technologies to Advance Automation in Forensic Science and Criminal Investigation
Source Author(s)/Editor(s): Chung-Hao Chen (Old Dominion University, USA), Wen-Chao Yang (National Central Police University, Taiwan)and Lijia Chen (Henan University, China)
DOI: 10.4018/978-1-7998-8386-9.ch010

Keywords: Criminal Science and Forensics / Digital Crime & Forensics / Information Science Reference / Security & Forensics

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Abstract

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.

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Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

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