Leveraging Rough Set Theory to Enhance the Performance of Financial Statement Fraud Detection Model
Pages 153 - 160
Abstract
In this study, we have developed a hybrid financial statement fraud detection model by combining rough set theory and ensemble learning. In this research, we have developed a pre-processing filter utilizing rough set theory to assist the model in selecting the most appropriate features. Additionally, we have also incorporated a feature extracted from the text information of the Management Discussion and Analysis (MD&A) section in the financial report, which effectively captures the positive tone expressed by the management. To address the challenge of imbalanced classes in fraud detection, we have applied the Synthetic Minority Oversampling Technique (SMOTE) algorithm. The optimization procedures significantly improved the performance of our model. We have compared our model to other regular machine learning methods and observed its superiority. The results demonstrate that the integration of rough set theory attribute reduction algorithms has substantially enhanced the performance of the ensemble learning model. Furthermore, the inclusion of the feature extracted from text information has proven to be effective in detecting financial fraud behaviors.
References
[1]
Baesens, B., S. Hoppner, and T. Verdonck, 2021 Data engineering for fraud detection. Decision Support Systems. 150: p. 13.
[2]
Hu, N., L. Liu, and V. Sambamurthy, 2011 Fraud detection in online consumer reviews. Decision Support Systems. 50(3): p. 614-626.
[3]
Pourhabibi, T., et al., 2020 Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems. 133: p. 15.
[4]
Dilla, W.N. and R.L. Raschke, 2015 Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems. 16: p. 1-22.
[5]
Papik, M. and L. Papikova, 2022 Detecting accounting fraud in companies reporting under US GAAP through data mining. International Journal of Accounting Information Systems. 45: p. 19.
[6]
van Capelleveen, G., et al., 2016 Outlier detection in healthcare fraud: A case study in the Medicaid dental domain. International Journal of Accounting Information Systems. 21: p. 18-31.
[7]
Choi, D. and K. Lee, 2018 An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation. Security and Communication Networks.
[8]
Omidi, M., et al., 2019 The Efficacy of Predictive Methods in Financial Statement Fraud. Discrete Dynamics in Nature and Society. 2019.
[9]
Ravisankar, P., et al., 2011 Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems. 50(2): p. 491-500.
[10]
Whiting, D.G., et al., 2012 MACHINE LEARNING METHODS FOR DETECTING PATTERNS OF MANAGEMENT FRAUD. Computational Intelligence. 28(4): p. 505-527.
[11]
Chen, S., 2016 Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus. 5(1): p. 89.
[12]
Hajek, P. and R. Henriques, 2017 Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowledge-Based Systems. 128: p. 139-152.
[13]
Bao, Y., et al., 2020 Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research. 58(1): p. 199-235.
[14]
Hajek, P. and R. Henriques, 2017 Mining corporate annual reports for intelligent detection of financial statement fraud - A comparative study of machine learning methods. Knowledge-Based Systems. 128: p. 139-152.
[15]
Perols, J.L., et al., 2017 Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction. Accounting Review. 92(2): p. 221-245.
[16]
Kim, Y.J., B. Baik, and S. Cho, 2016 Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Systems with Applications. 62: p. 32-43.
[17]
Zhou, W. and G. Kapoor, 2011 Detecting evolutionary financial statement fraud. Decision Support Systems. 50(3): p. 570-575.
[18]
Chawla, N.V., et al., 2002 SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 16: p. 321-357.
[19]
Pawlak, Z., 1982 Rough sets. International Journal of Computer & Information Sciences. 11(5): p. 341-356.
[20]
Chouchoulas, A. and Q. Shen, 2001 Rough set-aided keyword reduction for text categorization. Applied Artificial Intelligence. 15(9): p. 843-873.
[21]
Shen, Q. and A. Chouchoulas, 1999 Combining rough sets and data-driven fuzzy learning for generation of classification rules. Pattern Recognition. 32(12): p. 2073-2076.
[22]
BAO, Y., et al., 2020 Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research. 58(1): p. 199-235.
[23]
Ngai, E.W.T., et al., 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): p. 559-569.
[24]
Chen, Y.S. and Z.J. Wu, 2023 Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach. Sustainability. 15(1).
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- Leveraging Rough Set Theory to Enhance the Performance of Financial Statement Fraud Detection Model
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June 2024
1206 pages
ISBN:9798400710247
DOI:10.1145/3690407
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Published: 24 October 2024
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CAIBDA 2024: 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
June 21 - 23, 2024
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