The Role of AI in Forensic Accounting: Enhancing Fraud Detection Through Machine Learning
Keywords:
AI in forensic accounting, fraud detectionAbstract
Artificial Intelligence (AI) is revolutionizing forensic accounting by enhancing fraud detection and improving investigative accuracy. Through the application of machine learning algorithms, forensic accountants now have access to powerful tools that enable them to detect complex patterns, anomalies, and inconsistencies in financial data. These algorithms can process massive volumes of data, uncovering insights that would be challenging, if not impossible, to detect through traditional methods. Machine learning models, trained on historical fraud cases, can identify high-risk behaviors and irregular transaction patterns, allowing organizations to preemptively detect fraudulent activities. Furthermore, AI-powered systems can automate time-consuming tasks like data analysis and pattern recognition, freeing forensic accountants to focus on more nuanced investigative work. This automation not only speeds up the detection process but also enhances accuracy by reducing human error. Additionally, the predictive capabilities of machine learning support the development of proactive fraud prevention strategies, helping organizations to protect themselves against evolving fraud tactics. Despite these advancements, the integration of AI in forensic accounting also raises ethical and operational challenges, including data privacy concerns and the need for specialized training for accounting professionals. However, as AI technology matures, it is poised to become an indispensable tool in forensic accounting, empowering accountants with enhanced precision and speed in their investigations, ultimately contributing to a more robust financial ecosystem.
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References
Rezaee, Z., Wang, J., & Lam, B. (2018). Toward the integration of big data into forensic accounting education. Journal of Forensic and Investigative Accounting, 10(1), 87-99.
Wong, S., & Venkatraman, S. (2015). Financial accounting fraud detection using business intelligence. Asian Economic and Financial Review, 5(11), 1187.
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.
Parimi, S. S. (2017). Leveraging Deep Learning for Anomaly Detection in SAP Financial Transactions. Available at SSRN 4934907.
Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944.
Oyedokun, P., & Emmanuel, G. (2016). Forensic accounting investigation techniques: any rationalization?. Available at SSRN 2910318.
Ogiriki, T. O. N. Y. E., & Appah, E. (2018). Forensic accounting & auditing techniques on public sector fraud in Nigeria. International Journal of African and Asian Studies, 47(1), 10-19.
Asuquo, A. I. (2012). Empirical analysis of the impact of information technology on forensic accounting practice in Cross River State-Nigeria. International journal of scientific and technology research, 1(7), 25-33.
Bhasin, M. L. (2015). Contribution of forensic accounting to corporate governance: An exploratory study of an Asian country. International Business Management, 10(4), 2016.
Skalak, S. L., Golden, T. W., Clayton, M. M., & Pill, J. S. (2015). A guide to forensic accounting investigation. John Wiley & Sons.
Lu, F., Boritz, J. E., & Covvey, D. (2006). Adaptive fraud detection using Benford’s law. In Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006. Proceedings 19 (pp. 347-358). Springer Berlin Heidelberg.
Popoola, O. M. J. (2014). Forensic accountants, auditors and fraud capability and competence requirements in the Nigerian public sector (Doctoral dissertation, Universiti Utara Malaysia).
Mena, J. (2011). Machine learning forensics for law enforcement, security, and intelligence. CRC Press.
Ezeagba, C. E. (2014). The role of forensic accounting and quality assurance in financial reporting in selected commercial banks in Nigeria. International journal of economic development research and investment, 5(2), 20-31.
Bhasin, M. L. (2016). The fight against bank frauds: Current scenario and future challenges. Ciencia e Tecnica Vitivinicola Journal, 31(2), 56-85.
Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Naresh Dulam. Data Lakes Vs Data Warehouses: What’s Right for Your Business?. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Nov. 2016, pp. 71-94
Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93
Naresh Dulam, et al. Apache Arrow: Optimizing Data Interchange in Big Data Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 93-114
Naresh Dulam, and Venkataramana Gosukonda. Event-Driven Architectures With Apache Kafka and Kubernetes. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 115-36
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