On Data Reliability Assessment in Accounting Information Systems

Authors:
Ramayya Krishnan;James Peters;Rema Padman;David Kaplan
Affiliations:
The Heinz School, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890;The R. H. Smith School of Business, University of Maryland, College Park, Maryland 20742-7215;The Heinz School, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890;The Heinz School, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890
Venue:
Information Systems Research
Year:
2005

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Abstract

The need to ensure reliability of data in information systems has long been recognized. However, recent accounting scandals and the subsequent requirements enacted in the Sarbanes-Oxley Act have made data reliability assessment of critical importance to organizations, particularly for accounting data. Using the accounting functions of management information systems as a context, this paper develops an interdisciplinary approach to data reliability assessment. Our work builds on the literature in accounting and auditing, where reliability assessment has been a topic of study for a number of years. While formal probabilistic approaches have been developed in this literature, they are rarely used in practice. The research reported in this paper attempts to strike a balance between the informal, heuristic-based approaches used by auditors and formal, probabilistic reliability assessment methods. We develop a formal, process-oriented ontology of an accounting information system that defines its components and semantic constraints. We use the ontology to specify data reliability assessment requirements and develop mathematical-model-based decision support methods to implement these requirements. We provide preliminary empirical evidence that the use of our approach improves the efficiency and effectiveness of reliability assessments. Finally, given the recent trend toward specifying information systems using executable business process models (e.g., business process execution language), we discuss opportunities for integrating our process-oriented data reliability assessment approach-developed in the accounting context-in other IS application contexts.