Is fraud detection feasible without training data? Testing an expert-based approach
Abstract
We aim to derive a fraud detection approach applicable to conditions where historical fraud data is absent, inadequate, or outdated for making predictions. To this end, we propose a new approach to fraud detection based on expert opinion, enabling tailored tools for various conditions of economic/institutional environments. For this, we determined the relative importance of common fraud indicators based on a widely used model in the literature. We then used this information to formulate a scoring alternative to conventional versions, which uses either the original coefficients or the coefficients obtained from training the model. Finally, these scoring alternatives were compared by their detection performances. The design of this research demanded a multifaceted dataset consisting of expert opinions, financial statement data of non-financial companies in the Istanbul Stock Exchange, and local regulatory authority’s notifications on fraudulent companies. The analysis of the detection performances indicates that the proposed alternative scoring method poses a feasible alternative with competitive performance and fewer data requirements. This research’s approach sidesteps the training data requirement and provides financial analysts, auditors, and regulatory bodies a versatile classifier for various use cases regarding financial data, such as detecting fraudulent financial activity, as demonstrated in this study.
Keyword : financial statement fraud, fraud detection, beneish model, probit regression, expert knowledge, best-worst method, ROC-AUC analysis
How to Cite
Benligiray, S., Onay, A., & Yaşli Şen, F. (2025). Is fraud detection feasible without training data? Testing an expert-based approach. Journal of Business Economics and Management, 26(1), 210–230. https://doi.org/10.3846/jbem.2025.23443
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bao, Y., Ke, B., Li, B., Yu, Y. J., & 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
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Beneish, M. D., & Vorst, P. (2022). The cost of fraud prediction errors. Accounting Review, 97(6), 91–121. https://doi.org/10.2308/TAR-2020-0068
Beneish, M. D., Lee, C. M. & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57–82. https://doi.org/10.2469/faj.v69.n2.1
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Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317–331. https://doi.org/10.1016/j.ins.2019.05.042
Černevičienė, J., & Kabašinskas, A. (2022). Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Frontiers in Artificial Intelligence, 5, Article 827584. https://doi.org/10.3389/frai.2022.827584
Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146–1160. https://doi.org/10.1287/mnsc.1100.1174
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Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, Article 113421. https://doi.org/10.1016/j.dss.2020.113421
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28, 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44, 837–845. https://doi.org/10.2307/2531595
Dikmen, B., & Küçükkocaoğlu, G. (2010). The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming. Journal of Forecasting, 29(5), 442–466. https://doi.org/10.1002/for.1138
Duan, W., Hu, N., & Xue, F. (2024). The information content of financial statement fraud risk: An ensemble learning approach. Decision Support Systems, 182, Article 114231. https://doi.org/10.1016/j.dss.2024.114231
Durana, P., Blazek, R., Machova, V., & Krasnan, M. (2022). The use of Beneish M-scores to reveal creative accounting: Evidence from Slovakia. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(2), 481–510. https://doi.org/10.24136/eq.2022.017
Eining, M. M., Jones, D. R., & Loebbecke, J. K. (1997). Reliance on decision aids: An examination of auditors’ assessment of management fraud. Auditing: A Journal of Practice & Theory, 16(2), 1–19.
Fawcett, T. (2006). Introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Gepp, A., Kumar, K., & Bhattacharya, S. (2021). Lifting the numbers game: Identifying key input variables and a best‐performing model to detect financial statement fraud. Accounting & Finance, 61(3), 4601–4638. https://doi.org/10.1111/acfi.12742
Halilbegovic, S., Celebic, N., Cero, E., Buljubasic, E., & Mekic, A. (2020). Application of Beneish M-score model on small and medium enterprises in Federation of Bosnia and Herzegovina. Eastern Journal of European Studies, 11(1), 146–163.
Hamal, S., & Senvar, O. (2022). A novel integrated AHP and MULTIMOORA method with interval-valued spherical fuzzy sets and single-valued spherical fuzzy sets to prioritize financial ratios for financial accounting fraud detection. Journal of Intelligent & Fuzzy Systems, 42(1), 337–364. https://doi.org/10.3233/JIFS-219195
Hooda, N., Bawa, S., & Rana, P. S. (2018). Fraudulent firm classification: A case study of an external audit. Applied Artificial Intelligence, 32(1), 48–64. https://doi.org/10.1080/08839514.2018.1451032
Huang, S. Y., Lin, C. C., Chiu, A. A., & Yen, D. C. (2017). Fraud detection using fraud triangle risk factors. Information Systems Frontiers, 19, 1343–1356. https://doi.org/10.1007/s10796-016-9647-9
International Accounting Standards Board. (2018). The conceptual framework for financial reporting. London.
Kassem, R., & Higson, A. (2012). The new fraud triangle model. Journal of Emerging Trends in Economics and Management Sciences, 3(3), 191–195.
Khan, A. T., Cao, X., Li, S., Katsikis, V. N., Brajevic, I., & Stanimirovic, P. S. (2022). Fraud detection in publicly traded US firms using Beetle Antennae Search: A machine learning approach. Expert Systems with Applications, 191, Article 116148. https://doi.org/10.1016/j.eswa.2021.116148
Khatun, A., Ghosh, R., & Kabir, S. (2022). Earnings manipulation behavior in the banking industry of Bangladesh: The strategical implication of Beneish M-score model. Arab Gulf Journal of Scientific Research, 40(3), 302–328. https://doi.org/10.1108/AGJSR-03-2022-0001
Lappas, P. Z., & Yannacopoulos, A. N. (2021). A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment. Applied Soft Computing, 107, Article 107391. https://doi.org/10.1016/j.asoc.2021.107391
Lenard, M. J., & Alam, P. (2008). The use of fuzzy logic and expert reasoning for knowledge management and discovery of financial reporting fraud. In M. E. Jennex (Ed.), Knowledge management: Concepts, methodologies, tools, and applications (pp. 1013–1028). IGI Global. https://doi.org/10.4018/978-1-59904-933-5
Lin, C. C., Chiu, A. A., Huang, S. Y., & Yen, D. C. (2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowledge-Based Systems, 89, 459–470. https://doi.org/10.1016/j.knosys.2015.08.011
Lokanan, M. (2017). Theorizing financial crimes as moral actions. European Accounting Review, 27(5), 901–938. https://doi.org/10.1080/09638180.2017.1417144
Maniatis, A. (2022). Detecting the probability of financial fraud due to earnings manipulation in companies listed in Athens Stock Exchange Market. Journal of Financial Crime, 29(2), 603–619. https://doi.org/10.1108/JFC-04-2021-0083
Martínez-Camblor, P., & Pardo-Fernández, J. C. (2019). The Youden Index in the generalized receiver operating characteristic curve context. The International Journal of Biostatistics, 15(1), 1–20. https://doi.org/10.1515/ijb-2018-0060
Martinkutė-Kaulienė, R., Skobaitė, R., Stasytytė, V., & Maknickienė, N. (2021). Comparison of multicriteria decision-making methods in portfolio formation. Finance, Markets and Valuation, 7(2), 60–72. https://doi.org/10.46503/qwpu4486
Marqués, A. I., García, V., & Sánchez, J. S. (2020). Ranking-based MCDM models in financial management applications: Analysis and emerging challenges. Progress in Artificial Intelligence, 9(3), 171–193. https://doi.org/10.1007/s13748-020-00207
Mokrišová, M., & Horváthová, J. (2023). Domain knowledge features versus LASSO features in predicting risk of corporate bankruptcy – DEA approach. Risks, 11(11), Article 199. https://doi.org/10.3390/risks11110199
Morris, G. D. L. (2009). How a group of business students sold Enron a year before the collapse. Financial History, (Spring/Summer), 12–15.
Papík, M., & Papíková, L. (2020). Detection models for unintentional financial restatements. Journal of Business Economics and Management, 21(1), 64–86. https://doi.org/10.3846/jbem.2019.10179
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39–53. https://doi.org/10.1016/j.adiac.2010.10.004
Panigrahi, P. K. (2011, June). A framework for discovering internal financial fraud using analytics. In 2011 International Conference on Communication Systems and Network Technologies (pp. 323–327). IEEE. https://doi.org/10.1109/CSNT.2011.74
Qiu, S., He, H.-Q., & Luo, Y.-s. (2019). The value of restatement to fraud prediction. Journal of Business Economics and Management, 20(6), 1210–1237. https://doi.org/10.3846/jbem.2019.10489
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