Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process |
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| Institution: | 1. TU Dortmund University, Faculty of Business and Economics, Germany;2. The University of Tampa, Sykes College of Business, Department of Accounting, United States;1. College of Business Administration, University of Seoul, Seoulsiripdaero 163, Dongdaemun-gu, Seoul 02504, South Korea;2. Shidler College of Business, University of Hawaii at Manoa, 2404 Maile Way, Honolulu, HI 96822, United States;3. School of Management, Clark University, 950 Main Street, Worcester, MA 01610, United States;1. Florida State University, 1107 W. Call St., Tallahassee, FL 32306-4301, United States;2. Grand Valley State University, 3032 L William Seidman Center, 50 Front Ave SW, Grand Rapids, MI 49504-6424, United States;3. University of Tampa, JS 243, Mailbox O, 401 W. Kennedy Blvd., Tampa, FL 33606, United States;4. Florida State University, 346 RBB, 821 Academic Way, Tallahassee, FL 32306-1110, United States;5. Michigan State University, 632 Bogue St. Rm N220, East Lansing, MI 48824, United States;1. Strategic Security Sciences, Argonne National Laboratory, Ames, IA 50010, United States;2. Ivy College of Business, Iowa State University, Ames, IA 50010, United States;3. Strategic Security Sciences, Argonne National Laboratory, Argonne, IL 60439, United States;1. University of Waterloo, School of Accounting and Finance, 200 University Ave W, Waterloo, ON N2L 3G1, Canada;2. California State University Long Beach, College of Business, 1250 N Bellflower Blvd, Long Beach, CA 90815, USA;1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;2. Center of West African Studies, University of Electronic Science and Technology of China, Chengdu 611731, China;3. Department of Computer Science, University of Ghana, Ghana |
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| 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. |
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| Keywords: | Continuous control monitoring Internal audit Visualization Data analytics Healthcare Machine learning Information overload Payroll management |
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