The Rule-based Classification for the Embezzlement Detection using Case-based Reasoning
DOI:
https://doi.org/10.14738/abr.1402.19995Keywords:
embezzlement, Internal corruption, classification, criminology, case-based reasoningAbstract
Internal embezzlement is a major source of corporate losses. Companies are making various efforts to detect, prevent, and mitigate it. Embezzlement detection involves identifying financial irregularities through red flag behaviors, such as secretive work habits and unused vacation time. It also involves reviewing employee spending using machine learning-based transaction data analytics and forensic analysis. Furthermore, robust controls, such as double authorizations and regular audits, help detect issues such as fraudulent vendors, fake invoices, and misuse of company cards, thereby preventing significant financial losses. In this paper, we discuss a unique hybrid classification method for detecting embezzlement of corporations. We expect the proposed hybrid classification method to increase the accuracy of corporate embezzlement detection.
References
Abdallah, A., et al., Fraud detection system: a survey. Journal of Network and Computer Applications, 2016, Vol. 68.
Achakzai, M. and Juan, P., Using machine learning meta-classifiers to detect financial fraud. Finance Research Letters, 2022, Vol. 48.
ACFE, Occupational Fraud 2024: A Report to the Nations, 2024.
Ahmed, M., et al., A survey of anomaly detection techniques in the financial domain. Future Gener Comput System, 2026, 55.
Ali, A, et al., Financial fraud detection based on machine learning: a systematic literature review. Applied Science, 2022.
Albrecht, W., et al, Fraud Examination. Cengage, 6th, 2019.
Barletta, R., An introduction to case-based reasoning. AI Expert, 1991.
Brown, C. E. and Gupta, U., Applying case-based reasoning to the accounting domain. Intelligent Systems in Accounting, Finance, and Management, 1994, Vol 3.
Cho, J., The Process of Internal Corruption Detection using the Criminological Approach based on Case-based Reasoning, European Journal of Business and Management Research, 2024, 9(6).
Dantas, R., et al., Systematic acquired critique of credit card deception exposure through machine learning. Journal of Open Innovation Technology Market and Complexity, 2022, 8(4).
Hernandez Aros, L., et al., Financial fraud detection through the application of machine learning techniques: a literature review, Humanities and Social Sciences Communications, 2024.
Kumar, S., et al., Exploitation of machine learning algorithms for detecting financial crimes based on customers’ behavior. Sustainability, 2022, 14(21).
Meservy, R., et al., Internal control evaluation: A computational model of the review process. Auditing: A Journal of Practice & Theory, 1986, Vol. 6.
Morris, B. W., SCAN: A case-based reasoning model for generating information system control recommendations. International Journal of Intelligent Systems in Accounting, Finance and Management, 1994, Vol 3.
Nicholls, J., et al., Financial cybercrime: a comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. IEEE Access 9:163965–163986, 2021.
Reurink, A., Financial fraud: a literature review. Journal of Economic Surveys. 2018, 32(5). Scholar Publishing
Singh, A., et al, Financial fraud detection approach based on the firefly optimization algorithm and the support vector machine. Applied Computational Intelligence and Soft Computing, 2022.
West, J. and Bhattacharya, M., Intelligent financial fraud detection: a comprehensive review. Computers & Security, 2016, Vol. 57. Page | 78
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 June-Suh Cho

This work is licensed under a Creative Commons Attribution 4.0 International License.