HİLELİ FİNANSAL TABLOLARIN TESPİTİNDE VERİ MADENCİLİĞİ UYGULAMALARI: MEVCUT ARAŞTIRMA EĞİLİMLERİNİN İNCELENMESİ (2006-2024)

Abstract

Finansal piyasaların giderek karmaşıklaşması ve mevcut verilerin katlanarak artması, hileli finansal tablo tespitini kritik ve zor bir konu haline getirmiştir. Büyük hacimli verilerden ve finansal verilerin karmaşıklığından ilgili bilgiyi çıkarıp hileli finansal tablo tespit etmeye olanak sağlayan çeşitli veri madenciliği yöntemleri geliştirilmiştir. Son yıllarda veri madenciliği yöntemleri, hileli finansal tablo tespiti için en güvenilir yöntemlerden biri haline gelmiştir. Bu araştırmanın amacı, hileli finansal tablo tespitinde veri madenciliğinin uygulanmasına ilişkin mevcut araştırmalar hakkında bir inceleme sağlamak ve bulguları karşılaştırmaktır. Bu araştırmada bilimsel dergilerde yayınlanan, hileli finansal tablo tespitinde veri madenciliği yöntemlerini uygulayan araştırmaların nicelik açısından değerlendirilmesiyle birlikte araştırmacılara ve uygulayıcılara bir veri tabanı oluşturulması amaçlanmıştır. Araştırma sonucunda, en yaygın kullanılan veri madenciliği yöntemlerinin sırasıyla karar ağacı, destek vektör makineleri, lojistik regresyon ve yapay sinir ağı olduğu görülmüştür. Araştırmada en iyi performans gösteren yöntemlerin ise lojistik regresyon, hibrit modeller, sinir ağı ve XGBoost algoritmasının olduğu ve ele alınan araştırmaların %56,10’unda örneklem büyüklerinin 1.000’den az olduğu tespit edilmiştir.

Keywords

• Hile Tespiti
• Hileli Finansal Tablolar
• Veri Madenciliği
• Makine Öğrenimi
• Literatür Taraması

DATA MINING PRACTICES IN THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: A REVIEW OF THE CURRENT RESEARCH TRENDS (2006-2024)

Abstract

The increasing complexity of financial markets and the exponential increase in available data have made fraudulent financial statement detection a critical and difficult issue. Various data mining methods have been developed that make it possible to extract relevant information from large amounts of data and the complexity of financial data in order to recognize fraudulent financial statements. In recent years, data mining methods have become one of the most reliable methods for fraudulent financial statement detection. This study aims to provide a review of existing research on the application of data mining in fraudulent financial statement detection and compare the findings. This study aims to quantitatively evaluate research published in scientific journals that applies data mining methods to fraudulent financial statement detection and to create a database for researchers and practitioners. The study revealed that the most frequently used data mining methods were decision trees, support vector machines, logistic regression, and artificial neural networks. It was also detected that the best performing methods were logistic regression, hybrid models, neural network and XGBoost algorithm, and 56.10% of the studies had sample sizes less than 1,000.

Keywords

• Fraud Detection
• Fraudulent Financial Statements
• Data Mining
• Machine Learning
• Literature Review

References

1. ACFE. (2020). Report to the nations: 2020 global study on occupational fraud and abuse. https://legacy.acfe.com/report-to-the-nations/2020/ (Erişim Tarihi:09.04.2023).
2. AICPA. (2021). Consideration of fraud in a financial statement audit. https://us.aicpa.org/content/dam/aicpa/research/standards/auditattest/downloadabledocuments/au-00316.pdf (Erişim Tarihi: 10.04.2023).
3. Aksoy, B. (2021). Finansal tablo hileleri’nin makine öğrenmesi yöntemleri ve lojistik regresyon kullanılarak tahmin edilmesi: borsa İstanbul örneği. Maliye ve Finans Yazıları, (115), 27-58. https://doi.org/10.33203/mfy.733855
4. Albashrawi, M. (2016). Detecting financial fraud using data mining techniques: A decade review from 2004 to 2015. Journal of Data Science, 14(3), 553-569. https://doi.org/10.6339/JDS.201607_14(3).0010
5. Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402. https://doi.org/10.1016/j.cosrev.2021.100402
6. Ali, A. A., Khedr, A. M., El-Bannany, M., & Kanakkayil, S. (2023). A powerful predicting model for financial statement fraud based on optimized xgboost ensemble learning technique. Applied Sciences, 13(4), 2272. https://doi.org/10.3390/app13042272
7. An, B., & Suh, Y. (2020). Identifying financial statement fraud with decision rules obtained from Modified Random Forest. Data Technologies and Applications, 54(2), 235-255. https://doi.org/10.1108/DTA-11-2019-0208
8. Ashtiani, M. N., & Raahemi, B. (2022). Intelligent fraud detection in financial statements using machine learning and data mining: A systematic literature review. IEEE Access, 10, 72504-72525. https://doi.org/10.1109/ACCESS.2021.3096799

9. Ata, H. A. & Seyrek, İ. H. (2009). Hileli finansal tabloların tespitinde veri madenciliği tekniklerinin kullanımı: imalat firmaları üzerine bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (2) , 157-170.
10. Barman, S., Pal, U., Sarfaraj, M. A., Biswas, B., Mahata, A., & Mandal, P. (2016). A complete literature review on financial fraud detection applying data mining techniques. International Journal of Trust Management in Computing and Communications, 3(4), 336-359. https://doi.org/10.1504/IJTMCC.2016.084561
11. Beemamol, M. (2024). Mapping the trends of financial statement fraud detection research from the historical roots and seminal work. Journal of Economic Criminology, 6, 100096. https://doi.org/10.1016/j.jeconc.2024.100096
12. Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5(1), 89. https://doi.org/10.1186/s40064-016-1707-6
13. Chen, S., Goo, Y. J. J., & Shen, Z. D. (2014). A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements. The Scientific World Journal, 1, 1-9. https://doi.org/10.1155/2014/968712
14. Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421
15. Esen, M. F. (2016). Finansal suçların tespitinde veri madenciliği yaklaşımı ve literatüre bakış. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 11(2), 93-118.
16. Gaffaroglu, S., & Alp, S. (2023). Detecting frauds in financial statements: A comprehensive literature review between 2019 and 2023 (June). Pressacademia, 18(1), 47-51. https://doi.org/10.17261/Pressacademia.2023.1849
17. Gupta, R., & Gill, N. S. (2012). Prevention and detection of financial statement fraud – An implementation of data mining framework. International Journal of Advanced Computer Science and Applications, 3(8). https://doi.org/10.14569/IJACSA.2012.030825
18. Gupta, S., & Mehta, S. K. (2021). Data mining-based financial statement fraud detection: Systematic literature review and meta-analysis to estimate data sample mapping of fraudulent companies against non-fraudulent companies. Global Business Review, 25(5), 1290-1313. https://doi.org/10.1177/0972150920984857
19. Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139-152. https://doi.org/10.1016/j.knosys.2017.05.001
20. Hamal, S., & Senvar, O. (2021). Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs. International Journal of Computational Intelligence Systems, 14(1), 769-782. https://doi.org/10.2991/ijcis.d.210203.007
21. Jan, C. L. (2021). Detection of financial statement fraud using deep learning for sustainable development of capital markets under ınformation asymmetry. Sustainability, 13(17), 9879. https://doi.org/10.3390/su13179879
22. Javadian Kootanaee, A., Poor Aghajan, A. A., & Hosseini Shirvani, M. (2021). A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements. Journal of Optimization in Industrial Engineering, 14(2), 169-186. https://doi.org/10.22094/joie.2020.1877455.1685
23. Kılıç, İ., & Önal, S. (2022). Finansal hilelerin tespit edilmesinde kullanılan veri madenciliği yöntemleri ve borsa İstanbul’da bir uygulama. Muhasebe ve Denetime Bakış, 22(67), 181-208. https://doi.org/10.55322/mdbakis.1068503
24. Kırda, K., & Özçelik, M. K. (2021). Finansal tablo hilesi riski taşıyan şirketlerin veri madenciliği ile belirlenmesi. Muhasebe ve Vergi Uygulamaları Dergisi, 14(2), 609-639. https://doi.org/10.29067/muvu.802703
25. Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003. https://doi.org/10.1016/j.eswa.2006.02.016
26. Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining. International journal of computational intelligence, 3(2), 104-110.
27. Li, B., Yen, J., & Wang, S. (2024). Uncovering Financial Statement Fraud: A Machine Learning Approach With Key Financial Indicators and Real-World Applications. IEEE Access, 12, 194859-194870. https://doi.org/10.1109/ACCESS.2024.3520249
28. Li, W., Liu, X., & Zhou, S. (2024). Deep learning model based research on anomaly detection and financial fraud identification in corporate financial reporting statements. Journal of Combinatorial Mathematics and Combinatorial Computing, 123(1), 343-355. https://doi.org/10.61091/jcmcc123-24
29. 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
30. Liou, F. M. (2008). Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Auditing Journal, 23(7), 650-662. https://doi.org/10.1108/02686900810890625
31. Lokanan, M., & Sharma, S. (2024). The use of machine learning algorithms to predict financial statement fraud. The British Accounting Review, 57(1), 101560. https://doi.org/10.1016/j.bar.2025.101560
32. Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The 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
33. Nhien, C. T., Hung, D. N., & Bình, V. T. T. (2024). Using random forest and artificial neural network to detect fraudulent financial reporting: Data from listed companies in Vietnam. Calitatea, 25(202), 160-173. https://doi.org/10.47750/qas/25.202.17
34. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med 18(3): e1003583. https://doi.org/10.1371/journal.pmed.1003583
35. Papík, M., & Papíková, L. (2021). Application of selected data mining techniques in unintentional accounting error detection. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(1), 185–201. doi: 10.24136/eq.2021.007
36. Papík, M., & Papíková, L. (2022). Detecting accounting fraud in companies reporting under US GAAP through data mining. International Journal of Accounting Information Systems, 45, 100559. https://doi.org/10.1016/j.accinf.2022.100559
37. Phong, N. A., Tam, P. H., & Tung, N. T. (2024). Identifying fraud financial reports based on signs of income management using machine learning technology: the case of listed companies in Vietnam. Journal of International Commerce, Economics and Policy, 15(02), 2450013. https://doi.org/10.1142/S1793993324500133
38. Ramzan, S., & Lokanan, M. (2024). The application of machine learning to study fraud in the accounting literature. Journal of Accounting Literature. https://doi.org/10.1108/JAL-11-2022-0112
39. Rao, R. K., & Mandhala, V. N. (2024). Unveiling financial fraud: A comprehensive review of machine learning and data mining techniques. Ingenierie des Systemes d’Information, 29(06), 2309-2334. https://doi.org/10.18280/isi.290620
40. Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, 50(2), 491-500. https://doi.org/10.1016/j.dss.2010.11.006
41. Rezaee, Z. (2005). Causes, consequences, and deterence of financial statement fraud. Critical perspectives on Accounting, 16(3), 277-298. https://doi.org/10.1016/S1045-2354(03)00072-8
42. Riskiyadi, M. (2024). Detecting future financial statement fraud using a machine learning model in Indonesia: A comparative study. Asian Review of Accounting, 32(3), 394-422. https://doi.org/10.1108/ARA-02-2023-0062
43. Selimoğlu, S., & Altunel, M. (2019). Finansal tablo hileleri ve hile denetimi konusunda Türkiye’de hazırlanmış yüksek lisans/ doktora tezleri kapsamında akademik literatür taraması: 2008-2018. Muhasebe ve Finansman Dergisi, 127-140. https://doi.org/10.25095/mufad.606008
44. Shahana, T., Lavanya, V., & Bhat, A. R. (2023). State of the art in financial statement fraud detection: A systematic review. Technological Forecasting and Social Change, 192, 122527. https://doi.org/10.1016/j.techfore.2023.122527
45. Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. International Journal of Computer Applications, 39(1), 37-47. https://doi.org/10.5120/4787-7016
46. Singh Yadav, A. K., & Sora, M. (2022). Unsupervised learning for financial statement fraud detection using manta ray foraging based convolutional neural network. Concurrency and Computation: Practice and Experience, 34(27), e7340. https://doi.org/10.1002/cpe.7340
47. Song, X., Hu, Z., Du, J., & Sheng, Z. (2014). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China. Journal of Forecasting, 33(8), 611-626. https://doi.org/10.1002/for.2294
48. Sorkun, M. C., & Toraman, T. (2017). Fraud detection on financial statements using data mining techniques. Intelligent Systems and Applications in Engineering, 5(3), 132-134. https://doi.org/10.18201/ijisae.2017531428
49. SPK. Sermaye piyasasında bağımsız denetim standartları hakkında tebliğ, Seri: X, No:22. https://spk.gov.tr/data/635b6d211b41c62c78189ce9/Ba%C4%9F%C4%B1ms%C4%B1z%20Denetim%20Teblig%20(2).pdf, (Erişim Tarihi: 14.04.2023).
50. Sun, H., Li, J., & Zhu, X. (2023). Financial fraud detection based on the part-of-speech features of textual risk disclosures in financial reports. Procedia Computer Science, 221, 57-64. https://doi.org/10.1016/j.procs.2023.07.009
51. Sun, Y., Zeng, X., Xu, Y., Yue, H., & Yu, X. (2024). An intelligent detecting model for financial frauds in Chinese A‐share market. Economics & Politics, 36(2), 1110-1136. https://doi.org/10.1111/ecpo.12283
52. Tatar, B., & Kıymık, H. (2021). Finansal tablolarda hile riskinin tespit edilmesinde veri madenciliği yöntemlerinin kullanılmasına yönelik bir araştırma. Journal of Yaşar University, 16(64), 1700-1719. https://doi.org/10.19168/jyasar.956623
53. Terzi, S. (2012). Hile ve usulsüzlüklerin tespitinde veri madenciliğinin kullanımı. Muhasebe ve Finansman Dergisi, (54), 51-64.
54. Terzi, S., & Şen, İ. K. (2012). Finansal tablo hilelerinin veri madenciliği yardımıyla tespit edilmesi: üretim sektöründe bir araştırma. Muhasebe ve Vergi Uygulamaları Dergisi, 5(2), 25-40.
55. Tragouda, M., Doumpos, M., & Zopounidis, C. (2024). Identification of fraudulent financial statements through a multi‐label classification approach. Intelligent Systems in Accounting, Finance and Management, 31(2), e1564. https://doi.org/10.1002/isaf.1564
56. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence‐Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
57. Uğurlu, M., & Sevim, Ş. (2015a). Artificial neural network methodology in fraud risk prediction on financial statements; an emprical study in banking sector. İşletme Araştırmaları Dergisi, 7(1), 60-89. https://doi.org/10.20491/isader.2015115752
58. Uğurlu, M., & Sevim, Ş. (2015b). Finansal tablolardaki hile riskinin tahmin edilmesinde karma modellerin nispi başarısı üzerine karşılaştırmalı bir analiz. Gaziantep University Journal of Social Sciences, 14(1), 65-88. https://doi.org/10.21547/jss.256778
59. Wang, D., & Chen, L. (2024). Financial intelligence forecasting model on regression analysis and support vector machine. Journal of Network Intelligence (JNI), 9(3), 1388-1404.
60. West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66. https://doi.org/10.1016/j.cose.2015.09.005
61. Wyrobek, J. (2020). Application of machine learning models and artificial intelligence to analyze annual financial statements to identify companies with unfair corporate culture. Procedia Computer Science, 176, 3037-3046. https://doi.org/10.1016/j.procs.2020.09.335
62. Xiuguo, W., & Shengyong, D. (2022). An analysis on financial statement fraud detection for Chinese listed companies using deep learning. IEEE Access, 10, 22516–22532. https://doi.org/10.1109/access.2022.3153478
63. Yao, J., Pan, Y., Yang, S., Chen, Y., & Li, Y. (2019). Detecting fraudulent financial statements for the sustainable development of the socio-economy in China: a multi-analytic approach. Sustainability, 11(6), 1579. https://doi.org/10.3390/su11061579
64. Yue, D., Wu, X., Wang, Y., Li, Y., & Chu, C. H. (2007). A review of data mining-based financial fraud detection research. International Conference on Wireless Communications, Networking and Mobile Computing, 5519-5522. https://doi.org/10.1109/WICOM.2007.1352
65. Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision support Systems, 50(3), 570-575. https://doi.org/10.1016/j.dss.2010.08.007
66. Zhu, S., Wu, H., Ngai, E. W. T., Ren, J., He, D., Ma, T., & Li, Y. (2024). A financial fraud prediction framework based on stacking ensemble learning. Systems, 12(12), 588. https://doi.org/10.3390/systems12120588