International Peer-Reviewed JournalOpen AccessISSN 2456-8880
irejournals@gmail.com+91-7433024337

Home / Current Issue / Paper 1708448

1708448 Vol 5 · Issue 12 Download Paper

Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency

Titilayo Silifat Shehu

Subject area: Science,Engineering and Technology  ·  Area of research: Finance

Abstract

This paper examines the transformative impact of artificial intelligence on financial auditing practices, with particular focus on anomaly detection and fraud identification. As financial data volumes grow exponentially and transactions become increasingly complex, traditional sample-based auditing methods face significant limitations in providing comprehensive assurance. Through analysis of current applications and case studies, this research demonstrates how AI technologies?including machine learning algorithms, deep learning networks, natural language processing, and robotic process automation?are reshaping core audit functions from risk assessment to journal entry testing and revenue recognition. The integration of these technologies enables a shift from retrospective, sample-based verification toward comprehensive, real-time monitoring with predictive capabilities. The study identifies implementation challenges related to data quality, model explainability, skills gaps, and regulatory considerations, while providing practical solutions and frameworks for addressing these barriers. Key findings reveal significant improvements in audit efficiency, risk coverage, and anomaly detection across various organizational implementations. The research concludes that strategic AI integration, when coupled with appropriate human oversight and professional judgment, offers unprecedented opportunities to enhance audit quality while reducing fraud risk. As the auditing profession navigates this technological transformation, continued collaboration between practitioners, regulators, technologists, and educators will be essential to realize the full potential of AI-enhanced auditing.

Keywords

Artificial Intelligence; Machine Learning; Financial Auditing; Anomaly Detection; Continuous Auditing; Fraud Prevention

References

[1] Alles, M. G., Kogan, A., & Vasarhelyi, M. A. (2008). Putting continuous auditing theory into practice: Lessons from two pilot implementations. Journal of Information Systems, 22(2), 195-214.

[2] Argyrou, A. (2013). Auditing journal entries using self-organizing map. In Proceedings of the Eighteenth Americas Conference on Information Systems. Chicago, Illinois.

[3] Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451-468.

[4] Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2), 423-429.

[5] Davenport, T. H., & Raphael, J. (2017). Creating a cognitive audit. CFO Magazine, Q3, 35-39.

[6] Earley, C. E. (2015). Data analytics in auditing: Opportunities and challenges. Business Horizons, 58(5), 493-500.

[7] Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management, 23(3), 157-214.

[8] Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20.

[9] Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115-122.

[10] Luo, J., Meng, Q., & Cong, Y. (2018). Machine learning for fraud detection in financial statements. In 2018 International Conference on Computer Science and Application Engineering (CSAE), 1-5.

[11] 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.

[12] Schreyer, M., Sattarov, T., Borth, D., Dengel, A., & Reimer, B. (2019). Detection of anomalies in large scale accounting data using deep autoencoder networks. ArXiv Preprint, 1709.05254.

[13] Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89-109.

[14] Sun, T., & Vasarhelyi, M. A. (2018). Predicting credit card delinquencies: An application of deep neural networks. Intelligent Systems in Accounting, Finance and Management, 25(4), 174-189.

[15] Thiprungsri, S., & Vasarhelyi, M. A. (2011). Cluster analysis for anomaly detection in accounting data: An audit approach. International Journal of Digital Accounting Research, 11, 69-84.

[16] Vasarhelyi, M. A., Alles, M., & Williams, K. T. (2012). Continuous assurance for the now economy. A Thought Leadership Paper for the Institute of Chartered Accountants in Australia.

How to cite this paper

Titilayo Silifat Shehu "Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency" Iconic Research And Engineering Journals Volume 5 Issue 12 2022 Page 518-537
Titilayo Silifat Shehu "Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency" Iconic Research And Engineering Journals, vol. 5, no. 12, Jun. 2022
Titilayo Silifat Shehu (2022). Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency. Iconic Research And Engineering Journals, 5(12).
Titilayo Silifat Shehu "Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency" Iconic Research And Engineering Journals, vol. 5, no. 12, Jun. 2022.
@article{1708448,
      author = {Titilayo Silifat Shehu},
      title = {Integrating Artificial Intelligence in Financial Auditing: Enhancing Accuracy and Efficiency},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {12},
      pages = {518-537},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708448.pdf},
      abstract = {This paper examines the transformative impact of artificial intelligence on financial auditing practices, with particular focus on anomaly detection and fraud identification. As financial data volumes grow exponentially and transactions become increasingly complex, traditional sample-based auditing methods face significant limitations in providing comprehensive assurance. Through analysis of current applications and case studies, this research demonstrates how AI technologies?including machine learning algorithms, deep learning networks, natural language processing, and robotic process automation?are reshaping core audit functions from risk assessment to journal entry testing and revenue recognition. The integration of these technologies enables a shift from retrospective, sample-based verification toward comprehensive, real-time monitoring with predictive capabilities. The study identifies implementation challenges related to data quality, model explainability, skills gaps, and regulatory considerations, while providing practical solutions and frameworks for addressing these barriers. Key findings reveal significant improvements in audit efficiency, risk coverage, and anomaly detection across various organizational implementations. The research concludes that strategic AI integration, when coupled with appropriate human oversight and professional judgment, offers unprecedented opportunities to enhance audit quality while reducing fraud risk. As the auditing profession navigates this technological transformation, continued collaboration between practitioners, regulators, technologists, and educators will be essential to realize the full potential of AI-enhanced auditing.},
      keywords = {Artificial Intelligence; Machine Learning; Financial Auditing; Anomaly Detection; Continuous Auditing; Fraud Prevention},
      month = {June},
  }