Uncover and Identify Accounting Frauds in Publicly Traded Firms Using Machine Learning Techniques
Keywords:
Financial fraud, Fraud detection, fraud detection system, Machine learning, ensemble modelAbstract
Financial fraud has increased dramatically along with the rise of advanced technologies and worldwide connection. There are several types of financial fraud, each with its unique characteristics. This paper focuses on detecting accounting fraud in publicly traded firms. This study proposed a framework for financial fraud prediction and detection using machine learning (ML). This study utilized single ML models like Logistic Regression (LR), Naïve Byes (NB), Extreme Gradient Boosting (XG-BOOST), and ensemble techniques to identify fraud. Each classifier was assessed for accuracy, recall, precision, and testing and training time. The proposed ensemble classifier, which includes NB, LR, and XGBOOST, outperformed the single models by achieving accuracy, precision, and recall of 99.46%, 99.6%, and 99.82%, respectively. The findings suggest that the proposed ensemble model can forecast financial fraud more precisely and efficiently than other classifiers.
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B. Li and S. C. H. Hoi, “Online portfolio selection: A survey,” ACM Comput. Surv., vol. 46, no. 3, pp. 1-36, 2014 [doi:10.1145/2512962].
S. Emerson et al., “Trends and applications of machine learning in quantitative finance” in 8th international conference on economics and finance research (ICEFR 2019), 2019.
P. Ravisankar et al., “Detection of financial statement fraud and feature selection using data mining techniques,” Decis. Support Syst., vol. 50, no. 2, pp. 491-500, 2011 [doi:10.1016/j.dss.2010.11.006].
A. Abbasi et al., “Metafraud: A meta-learning framework for detecting financial fraud,” MIS Q., vol. 36, no. 4, pp. 1293-1327, 2012 [doi:10.2307/41703508].
Y. Bao et al., “Detecting accounting frauds in publicly traded US firms: New perspective and new method”. Available at: https://i/ssrn, Com/abstract 2670703, 2018.
B. Li et al., “Detecting accounting frauds in publicly traded US firms: A machine learning approach” in Asian Conference on Machine Learning, 2016, pp. 173-188. PMLR.
A. Dyck et al., “Who blows the whistle on corporate fraud?,” J. Fin., vol. 65, no. 6, pp. 2213-2253, 2010 [doi:10.1111/j.1540-6261.2010.01614.x].
Y. Bao et al., “Detecting accounting fraud in publicly traded US firms using a machine learning approach,” J. Acc. Res., vol. 58, no. 1, pp. 199-235, 2020 [doi:10.1111/1475-679X.12292].
B. Dorris, Report to the Nations, 2018 Global Study on Occupational Fraud and Abuse. New York: Association of Certified Fraud Examiners, 2018.
M. D. Beneish, “Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance,” J. Acc. Public Policy, vol. 16, no. 3, pp. 271-309, 1997 [doi:10.1016/S0278-4254(97)00023-9].
T. Y. Wang et al., “Corporate fraud and business conditions: Evidence from IPOs,” J. Fin., vol. 65, no. 6, pp. 2255-2292, 2010 [doi:10.1111/j.1540-6261.2010.01615.x].
D. W. Campbell and Ruidi Shang, “Tone at the bottom: Measuring corporate misconduct risk from the text of employee reviews,” Manag. Sci., vol. 68, no. 9, pp. 7034-7053, 2022 [doi:10.1287/mnsc.2021.4211].
M. Schneider and R. Brühl, “Disentangling the black box around CEO and financial information-based accounting fraud detection: Machine learning-based evidence from publicly listed US firms,” J. Bus. Econ., pp. 1-9, 2023.
J. O. Awoyemi et al., “Credit card fraud detection using machine learning techniques: A comparative analysis” in international conference on computing networking and informatics (ICCNI). IEEE, 2017, pp. 1-9 [doi:10.1109/ICCNI.2017.8123782].
A. Reurink, “Financial fraud: A literature review,” Contemp. Top. Fin. Collect. Lit. Surv., pp. 79-115, 2019.
X. Zhu et al., “Intelligent financial fraud detection practices in post-pandemic era,” Innovation (Camb), vol. 2, no. 4, 100176, 2021 [doi:10.1016/j.xinn.2021.100176].
A. Sudjianto et al., “Statistical methods for fighting financial crimes,” Technometrics, vol. 52, no. 1, pp. 5-19, 2010 [doi:10.1198/TECH.2010.07032].
[18] A. E. Omolara et al., “State-of-the-art in big data application techniques to financial crime: A survey,” Int. J. Comput. Sci. Netw. Sec., vol. 18, no. 7, pp. 6-16, 2018.
A. E. Omolara et al., “State-of-the-art in big data application techniques to financial crime: A survey,” Int. J. Comput. Sci. Netw. Sec., vol. 18, no. 7, pp. 6-16, 2018.
J. T. S. Quah and M. Sriganesh, “Real-time credit card fraud detection using computational intelligence,” Expert Syst. Appl., vol. 35, no. 4, pp. 1721-1732, 2008 [doi:10.1016/j.eswa.2007.08.093].
W. Richert, Building Machine Learning Systems with Python. Packt Publishing Ltd, 2013.
L. P. Coelho and W. Richert, Building Machine Learning Systems with Python. Packt Publishing Ltd, 2015.
M. Welling, A First Encounter with Machine Learning. Irvine, CA: University of California, 2011, p. 12.
M. Bowles, Machine Learning in Python: Essential Techniques for Predictive Analysis. John Wiley & Sons, 2015.
J. Han, M. Kamber in J. Pei, Data Mining: Concepts and Techniques: Concepts and Techniques, vol. 3. izd.", 2011.
I. H. Sarker et al., “Cybersecurity data science: An overview from machine learning perspective,” J. Big Data, vol. 7, pp. 1-29, 2020.
Y. Yusof et al., “Utilizing unsupervised weightless neural network as autonomous states classifier in reinforcement learning algorithm” in, 2017 IEEE 13th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE. IEEE, 2017, pp. 264-269 [doi:10.1109/CSPA.2017.8064963].
Z. Zhao and Tongyuan Bai, “Financial fraud detection and prediction in listed companies using SMOTE and machine learning algorithms,” Entropy (Basel), vol. 24, no. 8, p. 1157, 2022 [doi:10.3390/e24081157].
D. Chen, “Predicting accounting fraud in publicly traded Chinese firms via a PCA-RF method” in, Advances in Computer Science Research International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022). Atlantis Press, pp. 739-748, 2022 [doi:10.2991/978-94-6463-108-1_82].
P. Fukas et al., Augmenting Data with Generative Adversarial Networks to Improve Machine Learning-Based Fraud Detection, 2022.
T. H. Pranto, Kazi Tamzid Akhter Md Hasib, Tahsinur Rahman, Akm Bahalul Haque, AKM Najmul Islam, and Rashedur M. Rahman. "Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach." IEEE Access 10 (2022): 87115-87134.
E. Ileberi et al., “A machine learning based credit card fraud detection using the GA algorithm for feature selection,” J. Big Data, vol. 9, no. 1, pp. 1-17, 2022.
A. Hassanniakalager et al., “A machine learning approach to detect accounting frauds” Available at SSRN 4117764 (2022), SSRN Journal [doi:10.2139/ssrn.4117764].
M. Sánchez-Aguayo et al., “Predictive fraud analysis applying the fraud triangle theory through data mining techniques,” Appl. Sci., vol. 12, no. 7, p. 3382, 2022 [doi:10.3390/app12073382].
Saheed et al., “Big data analytics for credit card fraud detection using supervised machine learning models” in Big Data Analytics in the Insurance Market. Emerald Publishing Limited, 2022, pp. 31-56.
Z. Liu et al., Detecting Financial Statement Fraud with Interpretable Machine Learning, 2021.
S. Hamal and O. Senvar, “Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs,” Int. J. Comput. Intell. Syst., vol. 14, no. 1, pp. 769-782, 2021 [doi:10.2991/ijcis.d.210203.007].
N. Mqadi et al., “A SMOTe based oversampling data-point approach to solving the credit card data imbalance problem in financial fraud detection,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 277-286, 2021 [doi:10.12785/ijcds/100128].
N. K. Trivedi et al., “An efficient credit card fraud detection model based on machine learning methods,” Int. J. Adv. Sci. Technol., vol. 29, no. 5, pp. 3414-3424, 2020.
T. T. Nguyen et al., ‘Deep learning methods for credit card fraud detection.’ arXiv Preprint ArXiv:2012.03754, 2020.
A. Thennakoon et al., “Real-time credit card fraud detection using machine learning” in Data Sci. Eng. (Confluence) 9th International Conference on Cloud Computing. IEEE, 2019, pp. 488-493 [doi:10.1109/CONFLUENCE.2019.8776942].
P. Raghavan and N. E. El Gayar, “Fraud detection using machine learning and deep learning” in international conference on computational intelligence and knowledge economy (ICCIKE). IEEE, 2019, pp. 334-339 [doi:10.1109/ICCIKE47802.2019.9004231].
Available at: https://www.tipdm.org:10010/#/competition/1354705811842195456/question.
A. Argentiero et al., “The applications of artificial intelligence in cardiovascular magnetic resonance—A comprehensive review,” J. Clin. Med., vol. 11, no. 10, p. 2866, 2022 [doi:10.3390/jcm11102866].
A. Robles-Velasco et al., “Prediction of pipe failures in water supply networks using logistic regression and support vector classification,” Reliab. Eng. Syst. Saf., vol. 196, p. 106754, 2020 [doi:10.1016/j.ress.2019.106754].
A. Mehbodniya et al., “Financial fraud detection in healthcare using machine learning and deep learning techniques,” Sec. Commun. Netw., vol. 2021, pp. 1-8, 2021 [doi:10.1155/2021/9293877].
P. Hajek et al., “Fraud detection in mobile payment systems using an XGBoost-based framework,” Inf. Syst. Front., vol. 25, no. 5, pp. 1985-2003, 2023.
T. Chen and C. Guestrin, ‘XGBoost: A scalable tree boosting system. arXiv 2016.’ arXiv Preprint ArXiv:1603.02754, vol. 11, 2016.
N. Dhieb et al., “Extreme gradient boosting machine learning algorithm for safe auto insurance operations” in IEEE international conference on vehicular electronics and safety (ICVES). IEEE, 2019, pp. 1-5 [doi:10.1109/ICVES.2019.8906396].
H. Kaur et al., “A systematic review on imbalanced data challenges in machine learning: Applications and solutions,” ACM Comput. Surv., vol. 52, no. 4, pp. 1-36, 2020 [doi:10.1145/3343440].2nd ed., vol. 3, J. Peters, Ed. New York, NY, USA: McGraw-Hill, 1964, pp. 15–64. 2020 [doi:10.1145/3343440].
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