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Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process

Author

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  • Zhang, Guangyue
  • Atasoy, Hilal
  • Vasarhelyi, Miklos A.

Abstract

This paper presents a framework for proactive and intelligent continuous control monitoring (CCM) that helps management gain higher assurance over business processes and alleviate information overload. We adopt a design science approach towards systematically developing CCM artifacts, including operation and internal control violation display and multidimensional anomaly detection. We illustrate the design with an implementation project whereby a CPA firm, the firm's healthcare sector client, and the research team work together to improve the assurance provided by payroll reviews. This study contributes to the CCM literature by envisioning that interactive data visualization and machine learning technologies can alleviate information overload for management in CCM. Second, we provide real-world evidence on the improvement brought to economic and behavioral aspects of the control monitoring process compared to the traditional approach. We show that emerging technologies substantially improve the efficiency and effectiveness of risk assessment, anomaly detection, and loss prevention. We also contribute to control monitoring practice by providing guidance on artifact development and application for practitioners to follow.

Suggested Citation

  • Zhang, Guangyue & Atasoy, Hilal & Vasarhelyi, Miklos A., 2022. "Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    • Handle: RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000227
    DOI: 10.1016/j.accinf.2022.100570
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    References listed on IDEAS

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    1. Qiang Cheng & Beng Wee Goh & Jae B. Kim, 2018. "Internal Control and Operational Efficiency," Contemporary Accounting Research, John Wiley & Sons, vol. 35(2), pages 1102-1139, June.
    2. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    3. Vasarhelyi, Miklos A. & Alles, Michael & Kuenkaikaew, Siripan & Littley, James, 2012. "The acceptance and adoption of continuous auditing by internal auditors: A micro analysis," International Journal of Accounting Information Systems, Elsevier, vol. 13(3), pages 267-281.
    4. Chan, David Y. & Vasarhelyi, Miklos A., 2011. "Innovation and practice of continuous auditing," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 152-160.
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    Cited by:

    1. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    2. Wang, Wenru & Vasarhelyi, Miklos A., 2024. "The application of continuous audit and monitoring methodology: A government medication procurement case," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    3. Divya Prabha B & Akilashri P S S, 2025. "Improved Heuristic Algorithm and Optimal Incremental Learning for Dashboard Visualization and Healthcare Data Analysis on COVID Outbreaks," SN Operations Research Forum, Springer, vol. 6(3), pages 1-37, September.
    4. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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