Artificial Intelligence and Machine Learning in Forensic Accounting

International Journal of Computer Science and Engineering

© 2023 by SSRG - IJCSE Journal

Volume 10 Issue 7

Year of Publication : 2023

Authors : Avinash Malladhi

10.14445/23488387/IJCSE-V10I7P102

How to Cite?

Avinash Malladhi, "Artificial Intelligence and Machine Learning in Forensic Accounting," SSRG International Journal of Computer Science and Engineering , vol. 10,  no. 7, pp. 6-20, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I7P102

Abstract:

This paper reviews the application of artificial intelligence (AI) and machine learning (ML) for fraud detection in forensic accounting. We analyze commonly used supervised learning algorithms like support vector machines (SVMs), random forests, and neural networks. Unsupervised techniques are also discussed, including clustering, anomaly detection, and association rule mining. For feature engineering, natural language processing (NLP) enables the analysis of unstructured text data, while deep learning methods like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can extract features from raw data. Empirical results demonstrate the high accuracy of ensemble models combining multiple algorithms compared to individual models. However, challenges remain regarding model interpretability, bias, and regulatory compliance. Overall, AI and ML can enhance forensic accounting through automated analysis of massive datasets and identification of complex fraudulent patterns. Further research into ethical AI and standardized implementation is needed to realize the potential of these emerging technologies fully.

Keywords:

AI, Machine Learning, Forensic accounting & Fraud detection, Anti Money Laundering, Benford's law, Fraud triangle theory.

References:

[1] Carlene Beth Wynter, and Lynne Oats, "Don't Worry, We are not After You! Anancy Culture and Tax Enforcement in Jamaica," Critical Perspectives on Accounting, vol. 57, pp. 56-69, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] James A. DiGabriele, and Marianne Ojo, "Objectivity and Independence: The Dual Roles of External Auditors," Journal of Forensic & Investigative Accounting, vol. 6, no. 2, pp. 200-224, 2014.
[Google Scholar] [Publisher Link]
[3] Deniz Appelbaum et al., "Impact of Business Analytics and Enterprise Systems on Managerial Accounting," International Journal of Accounting Information Systems, vol. 25, pp. 29-44, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Federico Berruti, Emily Ross, and Allen Weinberg, "The Transformative Power of Automation in Banking," McKinsey & Company, 2017.
[Google Scholar] [Publisher Link]
[5] Y. Hou, S. Li, and M. Xia, "Forensic Accounting and Fraud Detection Using Artificial Intelligence Techniques," Journal of Forensic Accounting Research, vol. 3, no. 1, pp. A45-A64, 2018.
[6] Josh Baker, "Using Machine Learning to Detect Financial Statement Fraud," Business: Student Scholarship & Creative Works, 2019.
[Google Scholar] [Publisher Link]
[7] Xiao Ding et al., "Deep Learning for Event-Driven Stock Prediction," Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2327-2333, 2015.
[Google Scholar] [Publisher Link]
[8] Sepp Hochreiter, and Jürgen Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Brett Lantz, Machine Learning with R, Packt Publishing Ltd., 2013.
[Google Scholar] [Publisher Link]
[10] Richard J. Bolton, and David J. Hand, "Statistical Fraud Detection: A Review," Statistical Science, vol. 17, no. 3, pp. 235-249, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[11] J.R. Dorronsoro et al., "Neural Fraud Detection In Credit Card Operations," IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 827-834, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jarrod West, and Maumita Bhattacharya, "Intelligent Financial Fraud Detection: A Comprehensive Review," Computers & Security, vol. 57, pp. 47-66, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] S. B. Kotsiantis, "Supervised Machine Learning: A Review of Classification Techniques," Emerging Artificial Intelligence Applications in Computer Engineering, vol. 160, pp. 3-24, 2007.
[Google Scholar] [Publisher Link]
[14] Mark J. Nigrini, Benford's Law: Applications For Forensic Accounting, Auditing, And Fraud Detection, John Wiley & Sons, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jack Dorminey et al., "The Evolution of Fraud Theory," Issues in Accounting Education, vol. 27, no. 2, pp. 555-579, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] T. H. Davenport, and R. Kalakota, "The Potential for Artificial Intelligence in Banking," MIT Sloan Management Review, vol. 60, no. 4, pp. 59-62, 2019. [Online]. Available: https://sloanreview.mit.edu/article/the-potential-for-artificial-intelligence-in-banking/
[17] J. Adler, F. Schirmacher, and N. Peluso, "Artificial Intelligence in Forensic Accounting: Opportunities and Risks," Business Law Review, vol. 43, no. 2, pp. 255–269, 2019. [18] Efstathios Kirkos, Charalambos Spathis, and Yannis Manolopoulos, "Data Mining Techniques for the Detection of Fraudulent Financial Statements," Expert Systems with Applications, vol. 32, no. 4, pp. 995-1003, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Varun Chandola, Arindam Banerjee, and Vipin Kumar, "Anomaly Detection: a Survey," ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[20] J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297, 1967.
[Google Scholar] [Publisher Link]
[21] Larry M. Manevitz, and Malik Yousef, "One-Class SVMs for Document Classification," Journal of Machine Learning Research, vol. 2, pp. 139-154, 2001.
[Google Scholar] [Publisher Link]
[22] Xiaojin Jerry Zhu, "Semi-Supervised Learning Literature Survey," University of Wisconsin-Madison Department of Computer Sciences, vol. 1530, no. 3, 2005.
[Google Scholar] [Publisher Link]
[23] Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, "Semi-Supervised Learning," MITPress, 2006. [Publisher Link]
[24] Yoshua Bengio, and Yann LeCun, "Scaling Learning Algorithms Towards Ai," Large-Scale Kernel Machines, vol. 34, no. 5, pp. 1-41, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Richard S. Sutton, and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, vol. 1, no.1, 1998.
[Google Scholar] [Publisher Link]
[26] F. H. Tseng et al., "Dynamic Credit Card Fraud Detection Using Reinforcement Learning," International Conference on Information Security and Cryptology, Springer, pp 174-188, 2009.
[27] S. M. Thennakoon, R. K. Y. Li, and K. C. C. Chan, "A Hybrid Approach to Financial Fraud Detection: Integrating Statistical Methods with Machine Learning Techniques," Journal of Risk Management in Financial Institutions, vol. 13, no. 1, pp. 68-87, 2020.
[28] Abhinav Srivastava et al., "Credit Card Fraud Detection Using Hidden Markov Model," IEEE Transactions on Dependable and Secure Computing, vol. 5, no. 1, pp. 37-48, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Fawaz Mahiuob Mohammed Mokbal et al., "XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization," Journal of Information Security and Applications, vol. 58, p. 102813, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] K. Singh, P. Best, and C. Cheong, "Developing a Forensic Accounting Curriculum: A Case Study Using the Backward Design Process," Journal of Education for Business, vol. 93, no. 8, pp. 374-385, 2018.
[31] P. Ravisankar et al., "Detection of Financial Statement Fraud and Feature Selection using Data Mining Techniques," Decision Support Systems, vol. 50, no. 2, pp. 491-500, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Fabrizio Sebastiani, "Machine Learning in Automated Text Categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Luis Perez, and Jason Wang, "The Effectiveness of Data Augmentation in Image Classification Using Deep Learning," arXiv preprint arXiv:1712.04621, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Bing Liu, "Sentiment Analysis and Opinion Mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1- 167. 2012.
[Publisher Link]
[35] David M. Blei, Andrew Y. Ng, and Michael I. Jordan, "Latent Dirichlet Allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[Google Scholar] [Publisher Link]
[36] Tim Loughran, and Bill Mcdonald, "When is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-ks," The Journal of Finance, vol. 66, no. 1, pp. 35-65, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[37] David Nadeau, and Satoshi Sekine, "A Survey of Named Entity Recognition and Classification," Lingvisticae Investigationes, vol. 30, no. 1, no. 3-26. 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Jiwei Li, Alan Ritter, and Eduard Hovy, "Weakly Supervised User Profile Extraction From Twitter," Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 165-174, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[39] J.R. Dorronsoro et al., "Neural Fraud Detection In Credit Card Operations," IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 827-834, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Y. Cao et al., "Deep Learning for Fraud Detection in the Financial Industry: A Systematic Literature Review," Information Processing & Management, vol. 57, no. 5, 2020.
[41] Indranil Bose, and Radha K. Mahapatra, "Business Data Mining - A Machine Learning Perspective," Information & Management, vol. 39, no. 3, pp. 211-225, 2001. [CrossRef] [Google Scholar] [Publisher Link]
[42] W. Chen, and P. Y. Chau, "Using Neural Networks in the Analysis of Financial Health of Companies," 37th Annual Hawaii International Conference on System Sciences, 2004.
[43] David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, "Learning Representations By Back-Propagating Errors," Nature, vol. 323, no. 6088, pp. 533-536, 1986.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[Google Scholar] [Publisher Link]
[45] Donald R. Cressey, "Other People's Money: A Study in the Social Psychology of Embezzlement," Patterson Smith, 1953.
[Google Scholar] [Publisher Link]
[46] M. Chui et al., "The Future of Forensic Accounting: A Framework for Integrating Artificial Intelligence and Machine Learning," vol. 6, no. 1, pp. 1-35, 2021. [47] Karl Pearson, "On Lines and Planes of Closest Fit to Systems of Points in Space," Philosophical Magazine, vol. 2, no. 11, pp. 559–572, 1901.
[CrossRef] [Google Scholar] [Publisher Link]
[48] R. A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, vol. 7, no. 2, pp. 179–188, 1936.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Isabelle Guyon and Andre Elisseeff, "An Introduction to Variable and Feature Selection," Journal of Machine Learning Research, vol. 3, no. 3, pp. 1157–1182, 2003.
[Google Scholar] [Publisher Link]
[50] Ron Kohavi, and George H. John, "Wrappers for Feature Subset Selection," Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Robert Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society: Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[52] M. Richey, J. Morgan, and S. Li, "Ethical Artificial Intelligence for Fraud Prevention in Finance and Accounting," Ethics in Information Technology, vol. 22, no. 2, pp. 175–193, 2022.
[53] J. Zhang, A. Khan, and Q. Wu, "Deep Learning Models for Fraud Detection in Financial Statements," Proceedings under International Conference on Acoustics, Speech, and Signal Processing, pp. 2760-2764, 2021.
[54] Tom Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
[Google Scholar] [Publisher Link]
[56] Tom Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[57] S. B. Kotsiantis, I. D. Zaharakis, and P. E. Pintelas, "Machine Learning: A Review of Classification and Combining Techniques," Artificial Intelligence Review, vol. 26, no. 3, pp. 159–190, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[58] C. J. Van Rijsbergen, Information Retrieval, 2nd Edition, Butterworth- Heinemann, 1979.
[Publisher Link]
[59] Tom Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[60] N. V. Chawla et al., "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, no. 1, pp. 321–357, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Gustavo E. A. P. A. Batista, Ronaldo C. Prati, and Maria Carolina Monard, "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data," ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 20–29, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Haibo He, and Edwardo A. Garcia, "Learning from Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Z. Zhang, and M. Savvides, "Addressing Class Imbalance in Face Recognition: A Review," Artificial Intelligence Review, vol. 53, no. 2, pp. 993–1021, 2020.
[64] Charles Elkan, "The Foundations of Cost-Sensitive Learning," Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, WA, USA, pp. 973–978, 2001.
[Google Scholar] [Publisher Link]
[65] Hui Han, Wen-Yuan Wang, and Bing-Huan Mao, "Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning," Proceedings of the 2005 International Conference on Advances in Intelligent Computing, Hefei, China, pp. 878–887, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[66] S. Y. Seo, G. S. Jo, and S. H. Ha, "Random Under-Sampling Integrated with SMOTE for Imbalanced Data Classification," Proceedings of the 2016 International Conference on Information and Communication Technology Convergence, Jeju, South Korea, pp. 511– 513, 2016.
[67] Haibo He et al., "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning," Proceedings of the 2008 IEEE International Joint Conference on Neural Networks, Hong Kong, China, 2008, pp. 1322–1328, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz "A Multiple Resampling Method for Learning from Imbalanced Data Sets," Computational Intelligence, vol. 20, no. 1, pp. 18–36, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Gustavo Enrique A P A Batista, Ronaldo Cristiano Prati, and Maria Carolina Monard, "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data," ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 20–29, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Provost, and Foster, "Machine Learning from Imbalanced Data Sets 101," Proceedings of the AAAI 2000 Workshop on Imbalanced Data Sets, Austin, Texas, pp. 1–3, 2000.
[Google Scholar] [Publisher Link]
[71] Nitesh Vijay Chawla, Nathalie Japkowicz, and Aleksander R Kołcz, "Editorial: Special Issue on Learning from Imbalanced Data Sets," ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou, "Exploratory Undersampling for Class-Imbalance Learning," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539–550, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[73] G. Wu, and E. Y. Chang, "KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 786–795, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[74] K. M. Ting, Encyclopedia of Machine Learning, Claude Sammut, and Geoffrey I. Webb, Eds. Boston, MA: Springer, pp. 781–781, 2010.
[Publisher Link]
[75] Tom Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[76] John A. Swets, "Measuring the Accuracy of Diagnostic Systems," Science, vol. 240, no. 4857, pp. 1285–1293, 1988.
[CrossRef] [Google Scholar] [Publisher Link]
[77] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, New York, NY: Cambridge University Press, 2008.
[Google Scholar] [Publisher Link]
[78] Hugh Son, JPMorgan Marshals an Army of Developers to Automate High Finance, Bloomberg, 2017. [Online]. Available: https://www.bloomberg.com/news/articles/2017-02-28/jpmorgan-marshals-an-army-of-developers-to-automate-high-finance
[79] P. Lemieux, How PayPal Boosts Security with Artificial Intelligence, MIT Technology Review, 2018. [Online]. Available: https://www.technologyreview.com/2018/11/15/139164/how-paypal-boosts-security-with-artificial-intelligence/
[80] Mastercard, Mastercard Rolls Out Artificial Intelligence Across Its Global Network, 2016. [Online]. Available: https://newsroom.mastercard.com/press-releases/mastercard-rolls-out-artificial-intelligence-across-its-global-network
[81] HSBC, HSBC to Use AI to Detect Money Laundering, 2017. [Online]. Available: https://www.hsbc.com/news-and-insight/2017/hsbc-to-use-ai-to-detect-money-laundering
[82] T. H. Davenport, and R. Kalakota, The Potential for Artificial Intelligence in Banking, MIT Sloan Management Review, 2019. [Online]. Available: https://sloanreview.mit.edu/article/the-potential-for-artificial-intelligence-in-banking/
[83] S. Barocas, and A. D. Selbst, Big Data's Disparate Impact, California Law Review, vol. 104, no. 3, pp. 671-732, 2016. [Online]. Available: https://www.californialawreview.org/printarticle/big-datas-disparate-impact/
[84] Indre Zliobaite, "On the Relation Between Accuracy and Fairness in Binary Classification," Future of Computing & Informatics Journal, vol. 1, no. 1, pp. 45-54, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[85] Davide Castelvecchi, “Can We Open the Black Box of AI?,” Nature, vol. 538, no. 7623, pp. 20-23, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[86] Alejandro Barredo Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI," Information Fusion, vol. 58, pp. 82-115, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[87] Brent Daniel Mittelstadt et al., "The Ethics of Algorithms: Mapping the Debate," Big Data & Society, vol. 3, no. 2, pp. 1-21. 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[88] Thomas H. Davenport, and Julia Kirby, Just How Smart are Smart Machines?, MIT Sloan Management Review, vol. 57, no.3, pp. 21- 25, 2016. [Online]. Available: https://sloanreview.mit.edu/article/just-how-smart-are-smart-machines/
[89] Lee A. Bygrave, "Data Protection by Design and by Default: Deciphering the Eu's Legislative Requirements," Oslo Law Review, vol. 6, no. 1, pp. 105-120. 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[90] S. Moral et al., "An Ethical Framework for the Digital Afterlife Industry," Science and Engineering Ethics, vol. 24, no. 4, pp. 1219- 1242, 2018.
[91] Cohen, and M. Zimbelman, "Forensic Accounting and Fraud Detection: The Role of Artificial Intelligence and Machine Learning," Journal of Forensic Accounting Research, vol. 7, no. 2, pp. 123-136, 2014.
[92] Stuart J. Russell, and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2020.
[Google Scholar] [Publisher Link]
[93] K. N. K. R. Babu, "Emerging Technologies for Forensic Accounting: A Review," International Journal of Accounting and Financial Reporting, vol. 9, no. 1, pp. 134-145, 2019.
[94] Philip Treleaven, Richard Gendal Brown, and Danny Yang, "Blockchain Technology in Finance," Computer, vol. 50, no. 9, pp. 14-17, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[95] M. Alles, "Continuous Auditing and Reporting Systems: Implications for Assurance and Decision Making," International Journal of Accounting Information Systems, vol. 30, pp. 1-18, 2019.
[96] M. Vasarhelyi, and F. B. Roman, "Continuous Auditing in the Twenty- First Century: Lessons from Three Decades of Research and Practice," Advances in Accounting, vol. 47, p. 100420, 2019.
[97] R. E. Severson, "Data Analysis and Visualization for Forensic Accounting," Handbook of Forensic Accounting, M. K. Badawy, Ed. Wiley, pp. 317-332, 2021.
[98] S. Bhattacharya, S. Sengupta, and S. Saha, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques, 1st ed. Wiley, 2015, pp. 167-186.
[99] M. K. Badawy, Fraud Detection and Prevention, Handbook of Forensic Accounting, M. K. Badawy, Ed. Wiley, pp. 57-76, 2021.
[100] D. L. Silver, "Personalized Learning and Training with Machine Learning," Proceedings of the 24th Annual ACM Symposium on Applied Computing, pp. 45-49, 2009.
[101] M. Reigeluth, Instructional-Design Theories and Models, Volume IV: The Learner-Centered Paradigm of Education, 2017.
[102] Carr-Chellman, Eds. Routledge, pp. 49-64, 2017.
[103] G. Wang et al., "Decision Support Systems Driven by Machine Learning," Decision Support Systems, vol. 124, pp. 113078, 2019.
[104] Richard J. Boltonm, and David J. Hand, "Statistical Fraud Detection: A Review," Statistical Science, vol. 17, no. 3, pp. 235-255, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[105] Richard S. Sutton, and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.
[Google Scholar] [Publisher Link]
[106] Volodymyr Mnih et al., "Human-Level Control through Deep Reinforcement Learning," Nature, vol. 518, no. 7540, pp. 529-533, 2015.
[Google Scholar] [Publisher Link]
[107] J. X. Chen et al., "Adaptive Fraud Detection Using Reinforcement Learning in Forensic Accounting," Proceedings of the 29th International Conference on Computational Intelligence and Software Engineering, pp. 315-320, 2020.
[108] Sinno Jialin Pan and Qiang Yang, "A Survey on Transfer Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[109] Rie Kubota Ando, and Tong Zhang, "A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data," Journal of Machine Learning Research, vol. 6, pp. 1817-1853, 2005.
[Google Scholar] [Publisher Link]
[110] Jeff Donahue et al., "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition," Proceedings of the 31st International Conference on Machine Learning, 2014, pp. 647- 655.
[Google Scholar] [Publisher Link]
[111] H. Brendan McMahan et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017, pp. 1273- 1282.
[Google Scholar] [Publisher Link]
[112] R. K. Chinthala, and M. V. N. A. Prasad, "Secure Federated Learning for Fraud Detection in Banking and Finance," International Journal of Intelligent Systems and Applications, vol. 12, no. 10, pp. 68-77, 2020.
[113] Qiang Yang et al., "Federated Machine Learning: Concept and Applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 12:1-12:19, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[114] David Gunning, and David W. Aha, "DARPA's Explainable Artificial Intelligence (XAI) Program," AI Magazine, vol. 40, no. 2, pp. 44-58, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[115] F. Pasquini, M. L. P. Sá, and A. de Barros, "Explainable Artificial Intelligence (XAI) in Forensic Accounting: A Review of the Literature and Future Directions," Proceedings of the 8th International Conference on Information Systems and Technology Management, pp. 117-128, 2021.
[116] Amina Adadi and Mohammed Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138-52160, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[117] W. Steve Albrecht, Chad Albrecht, and Conan C. Albrecht, "Current Trends in Fraud and Its Detection," Information Security Journal: A Global Perspective, vol. 16, no. 1, pp. 2-12, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[118] E. Aleskerov, B. Freisleben, and B. Rao, "CARDWATCH: A Neural Network-Based Database Mining System for Credit Card Fraud Detection," Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, pp. 220- 226.1997.
[CrossRef] [Google Scholar] [Publisher Link]
[119] Varun Chandola, Arindam Banerjee, and Vipin Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[120] Yoshua Bengio, Aaron Courville, and Pascsl Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[121] N. V. Chawla et al., "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[122] M. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[123] Pratyusa K. Manadhata and Jeannette M. Wing, "An Attack Surface Metric," IEEE Transactions on Software Engineering, vol. 37, no. 3, pp. 371-386, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[124] Mark J. Nigrini, "Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations," Wiley, 2011.
[Google Scholar] [Publisher Link]
[125] Fred H. Cate, "The Failure of Fair Information Practice Principles," Consumer Protection in the Age of the Information Economy, J. K. Winn, Ed. Ashgate, pp. 341-377, 2006.
[Google Scholar] [Publisher Link]
[126] J. Lee, R. Singh, and T. Tan, "A Review of AI and Machine Learning Models for Financial Fraud Detection," Proceedings of International Conference on Machine Learning Applications, pp. 210-216, 2021.
[127] Kirkos, "Unsupervised Learning with Clustering Algorithms for Credit Card Fraud Detection," Proceedings of IEEE Symposium Computational Intelligence in Cyber Security, pp. 45-51, 2020.
[128] M. Hagenau, B. Hedrich, and J. Neumann, "Automated Detection of Procurement Fraud using Association Rule Mining," IEEE Transactions on Computational Social Systems, vol. 6, no. 6, pp. 1455-1465, 2019.
[129] R. Fuller, D. Pai, and R. Kumar, "Document Fraud Detection Using Linguistics and SVM," Proceedings of IEEE Security and Privacy Workshops (SPW), pp. 182-188, 2020.