APPLICATION OF NEURAL NETWORKS AND BIG DATA TECHNOLOGIES IN DETECTING ANOMALIES IN FINANCIAL STATEMENTS
Article Sidebar
Main Article Content
Abstract
This study explores the application of neural networks and Big Data technologies in detecting anomalies in financial statements. The research focuses on developing countries, particularly Uzbekistan, where the accuracy, transparency, and reliability of financial reporting need improvement. The study analyzes the advantages and limitations of artificial intelligence models and their practical significance for government agencies, banks, and foreign investors. Furthermore, the research highlights how neural networks can identify unusual financial patterns early, facilitating faster decision-making.
Article Details
References
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
Benish, M. (1999). The detection of earnings manipulation. Journal of Accounting Research, 37(1), 1–33.
Dechow, P., Ge, W., & Schrand, C. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.
Kogan, A., Sudit, E., & Vasarhelyi, M. (2014). Continuous auditing using Big Data. International Journal of Accounting Information Systems, 15(3), 187–205.
Müller, R., Schreyer, M., Sattarov, T., & Borth, D. (2022, November). RESHAPE: explaining accounting anomalies in financial statement audits by enhancing SHapley additive explanations. In Proceedings of the Third ACM International Conference on AI in Finance (pp. 174-182).
Roxas, M. L. (2011). Financial statement fraud detection using ratio and digital analysis. Journal of Leadership, Accountability, and Ethics, 8(4), 56-66.
Zhang, Y., Chen, X., & Liu, J. (2020). Financial fraud detection using neural networks. Expert Systems with Applications, 159, 113–128.