The Applications of Data Mining Techniques in Detecting Occupational Fraud: A Qualitative Review of Forensic Accounting Practices

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Authors
Thomas, Nissa Sara
Issue Date
2024
Degree
MSc in International Accounting and Finance
Publisher
Dublin Business School
Rights
Abstract
This study examines the application of data mining techniques to forensic accounting in terms of fraud detection. The research methodology involves qualitative methods, which include case studies and in-depth interviews with veteran forensic accountants concerning how data mining tools are used to perform their work. It then compares the results of such applications to those obtained through traditional forms of expert opinion analysis. It becomes clear from the study that traditional accounting methods are powerful but often inadequate for handling big data and unable to do real-time analysis. One of the most important topics highlighted in this research is deep learning and artificial intelligence, which are providing increasingly complex fraud schemes with powerful tools. Also, it examines the ethical and legal aspects of data mining in fraud detection. This research makes a contribution to the academic debate on forensic accounting and data mining, offering practical advice for improving occupational fraud detection capabilities.