Artificial Intelligence for detecting and preventing procurement fraud

Authors

  • Chiji Longinus Ezeji School of Public Management Governance and Public Policy, College of Business and Economics, University of Johannesburg, South Africa https://orcid.org/0000-0003-4732-0485

DOI:

https://doi.org/10.36096/ijbes.v6i1.477

Keywords:

Artificial, Detection, Disruptive, Fraud, Intelligence, Prevention and Technolog

Abstract

The utilization of powerful machine learning models in artificial intelligence offers novel prospects for the identification of fraudulent activities. Artificial Intelligence (AI) is a revolutionary technological tool that enhances the ability to detect and prevent fraud by improving efficiency and effectiveness. This research offers a thorough examination of the utilization of artificial intelligence technology in the realm of procurement fraud prevention and detection. Additionally, it highlights the obstacles that arise when employing machine learning techniques for the purpose of identifying and preventing fraudulent activities. A mixed methods approach was employed in this study, wherein data was collected through an unstructured interview and questionnaire. We conducted a comprehensive review of relevant scholarly articles and online resources. The findings indicate that fraudsters are progressively advancing in their skills, which poses a significant challenge in detecting fraudulent activities. The advent of AI in fraud detection has demonstrated its transformative impact. AI has achieved unparalleled precision and velocity in crime detection and prevention, surpassing the capabilities of any human. Artificial intelligence (AI) enhances the capacity for automation. Accessing unstructured data in the form of spreadsheets, digital documents, and email inboxes poses a significant issue for the procurement function. In order to achieve a successful procurement transformation, businesses should prioritize the creation of essential tools, guarantee acceptance through the establishment of a superior user experience, and integrate both novel and pre-existing technology. It is imperative to disseminate knowledge to the general public regarding the escalating sophistication of artificial intelligence (AI) in the realm of fraud detection and prevention. This includes elucidating the potential benefits of AI in identifying patterns of suspicious activities, assessing its efficacy in predicting potential threats or fraudulent activities prior to their manifestation, and exploring its utility in analyzing historical data pertaining to both familiar and unfamiliar forms of fraudulent behavior.

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References

Ajzen, I. (2020). Theory of Planned Behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195. DOI: https://doi.org/10.1002/hbe2.195

Amiram, D., Bozanic, Z., & Rouen, E. (2015). Financial Statement Errors: Evidence from the distributional properties of financial statement numbers. A review of Accounting Studies, 20, (15),40–1593. https://doi.org/10.1007/s11142-015-9333-z. DOI: https://doi.org/10.1007/s11142-015-9333-z

Bao, Y., Hilary, G., and Ke-Bin. (2020). Artificial Intelligence and Fraud Detection. Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, forthcoming, Springer Nature. Avaliable at https://ssrn.com/abstract=3738618 or http://dx.doi.org/10.2139/ssrn.3738618 DOI: https://doi.org/10.2139/ssrn.3738618

Bekker, J. & Davis, J. (2020). Learning From Positive and unlabeled data: A survey. Machine Learn., 109, (4), 719–760. https://doi.org/10.1007/s10994-020-05877-5 DOI: https://doi.org/10.1007/s10994-020-05877-5

Boute R. N., Gijsbrechts J., & Van Mieghem J. A. (2022). Digital Lean Operations: Smart automation and artificial intelligence in financial services. In V. Babich, J. Birge, & G. Hilary (Eds.) Innovative technology at the interface of finance and operations. Springer Series in Supply Chain Management. Springer Nature. https://doi.org/10.1287/msom.2021.1064. DOI: https://doi.org/10.1007/978-3-030-75729-8_6

Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What are You Saying? Using topic to detect financial misreporting. Journal of Accounting Research, 5 (8), 237–291.https://doi.org/10.1111/1475-679X.12294. DOI: https://doi.org/10.1111/1475-679X.12294

Edwin, H., Sutherland and Donald, R., Cressey, Lippincott. (1978). Criminology. (Eds.), University of California: Santa Barbra.

Ernst, E & Young, Y. (2010). Driving Ethical Growth new markets, new challenges. 11th Global Fraud Survey. https://www.compliance building.com

Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using Generative Adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 4 (79), 448-455. https://doi.org/10.1016/j.ins.2017.12.030. DOI: https://doi.org/10.1016/j.ins.2017.12.030

Fraud Fighter. (2023) Procurement Fraud Statistic. Available: http://www.fraudfighters.net/news what-is-procurement-fraud. https://www.fraudfighters.net/news

J. Guo, G. Liu, Y. Zuo, J. Wu. (2018). Learning Sequential Behavior Representations for Fraud Detection, 2018, IEEE International Conference on Data Mining (ICDM), Singapore, 2018, pp. 127-136, https://doi.org/10.1109/ICDM.2018.00028. DOI: https://doi.org/10.1109/ICDM.2018.00028

Hastie, T., R. Tibshirani, and J.H. Friedman. (2019). The Elements of Statistical Learning. New York: Springer.

Morency L. P. (2018). Multi-modal Machine Learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607. DOI: https://doi.org/10.1109/TPAMI.2018.2798607

K. Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). Application of Data Mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3),559-569. https://doi.org/10.1016/j.dss.2010.08.006. DOI: https://doi.org/10.1016/j.dss.2010.08.006

The Association of Certified Fraud Examiners (2016). Report to the Nations on Occupation Fraud and Abuse and Asset Misappropriation.2016, ACFE Report to the Nations Charts. Avaliable :https://www.acfeinsights.com/acfe-insights.

Wang Y., Wang L., Li Y., He D., Chen W., Liu T. Y. (2013). A Theoretical Analysis of NDCG Ranking Measures: Proceedings of the 26th Annual Conference on Learning Theory, 21(3), 321-342. https://doi.org/10.48550/arXiv.1304.6480.

Wang, J., Wen, R., Wu, C., Huang, Y., & Xion, J. (2019). Fraudster Detection via Graph convolutional networks in online app review system. World Wide Web Conference, 13 (17), 310-316. https://doi.org/10.1145/3308560.3316586. DOI: https://doi.org/10.1145/3308560.3316586

Wang, Y. & Xu, W. (2018). Leveraging Deep Learning with LDA- based text analytics to detect automobile insurance fraud. Decision Support Systems, 10(5),87-95. https://doi.org/10.1016/j.dss.2017.11.00 DOI: https://doi.org/10.1016/j.dss.2017.11.001

Whiting D.G., Hansen J.V., McDonald J.B., Albrecht C., and Albrecht W.S. (2012). Machine Learning Methods for Detecting Patterns of Management Fraud. Computational Intelligence, 2 (8),505–Zhang, J. (2020). 527. https://doi.org/10.1111/j.1467-8640.2012.00425.x. DOI: https://doi.org/10.1111/j.1467-8640.2012.00425.x

Zhang, J. (2020). Detecting Accounting Fraud in Publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292. DOI: https://doi.org/10.1111/1475-679X.12292

Zheng, P., Yuan, S., Wu, X., Li, J., & Lu, A. (2019). One-class Adversarial Bonnets for Fraud Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 12 (86),12-93. https://www.paperdigest.org.

Zhong, Q., Liu, Y., Ao, X., Hu, B., Feng, J., Tang, J. (2020). Financial Defaulter Detection on Online Credit Payment via multi-view attributed heterogeneous information network. Proceedings of the Web Conference, 2 (20), 785- 795. https://doi.org/10.1145/3366423.3380159. DOI: https://doi.org/10.1145/3366423.3380159

Zhu, Y., Xi, D., Song, B., Zhuang, F., Chen, S., Gu, X. (2020). Modeling Users’ Behavior Sequences with hierarchical explainable network for cross-domain fraud detection. Proceedings of The Web Conference 2(20), 928–938. https://doi.org/10.1145/3366423.3380159. DOI: https://doi.org/10.1145/3366423.3380172

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Published

2024-03-23

How to Cite

Ezeji, C. L. (2024). Artificial Intelligence for detecting and preventing procurement fraud. International Journal of Business Ecosystem & Strategy (2687-2293), 6(1), 63–73. https://doi.org/10.36096/ijbes.v6i1.477

Issue

Section

Interdisciplinary Studies in Business Ecosystem