Managing Data Quality Risk in Accounting Information Systems

Xue Bai (), Manuel Nunez () and Jayant R. Kalagnanam ()
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Xue Bai: Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269
Manuel Nunez: Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269
Jayant R. Kalagnanam: IBM T. J. Watson Research Center, Yorktown Heights, New York 10598

Information Systems Research, 2012, vol. 23, issue 2, 453-473

Abstract: The quality of data contained in accounting information systems has a significant impact on both internal business decision making and external regulatory compliance. Although a considerable body of literature exists on the issue of data quality, there has been little research done at the task level of a business process to develop effective control strategies to mitigate data quality risks. In this paper, we present a methodology for managing the risks associated with the quality of data in accounting information systems. This methodology first models the error evolution process in transactional data flow as a dynamical process; it then finds optimal control policies at the task level to mitigate the data quality-related risks using a Markov decision process model with risk constraints. The proposed Markov decision methodology facilitates the modeling of multiple dimensions of error dependence, captures the correlated impact among control procedures, and identifies an optimal control policy. A revenue realization process of an international production company is used to illustrate this methodology.

Keywords: data quality; risk; audit; control; accounting information systems; constrained Markov decision processes (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:23:y:2012:i:2:p:453-473

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