Abstract
Fraud exists in all walks of life and detecting and preventing fraud represents an important research question relevant to many stakeholders in society. With the rise in big data and artificial intelligence, new opportunities have arisen in using advanced machine learning models to detect fraud. This chapter provides a comprehensive overview of the challenges in detecting fraud using machine learning. We use a framework (data, method, and evaluation criterion) to review some of the practical considerations that may affect the implementation of machine-learning models to predict fraud. Then, we review select papers in the academic literature across different disciplines that can help address some of the fraud detection challenges. Finally, we suggest promising future directions for this line of research. As accounting fraud constitutes an important class of fraud, we will discuss all of these issues within the context of accounting fraud detection.
We thank Kai Guo for research assistance.
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Notes
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See Boute et al. (2022) for a more in-depth discussion on the use of AI in financial services.
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In a 10-year review of corporate accounting fraud commissioned by the Committee of Sponsoring Organization of the Treadway Commission (COSO), Beasley et al. (2010) find that the total cumulative misstatement or misappropriation of nearly $120 billion across 300 fraud cases with available information (mean of nearly $400 million per case) (Beasley et al., 1999).
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See SAS no. 99 (American Institute of Certified Public Accountants, 2002) for a discussion of this issue in a U.S. context.
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See Zhang et al. (2015) for a good discussion of these issues.
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Supervised models “learn” from labeled data. To train a supervised model, one presents both fraudulent and non-fraudulent records that have been labeled as such. Unsupervised models ask the model to “learn” the data structure on its own.
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This section heavily relies on Bao et al. (2020).
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This section borrows heavily from the online appendix of Bao et al. (2020).
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Recurrent neural networks are artificial neural networks where connections between nodes form a directed graph along a temporal sequence.
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Bao, Y., Hilary, G., Ke, B. (2022). Artificial Intelligence and Fraud Detection. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-75729-8_8
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