Exception Prioritization in Continuous Auditing: A Framework and Experimental Evaluation

Abstract

Researchers have found that the volume of exceptions generated by a continuous auditing system can be overwhelming for an internal audit department to investigate. In this paper, we propose a framework that systematically prioritizes exceptions based on the likelihood of an exception being erroneous or fraudulent. The framework consists of six stages: 1) generation of exceptions using defined rules, 2) assignment of suspicious scores to exceptions using belief functions, 3) exception prioritization, 4) exception investigation by auditors, 5) rule confidence refinement by back propagation, and 6) rule additions by a rule learner algorithm. We also simulated the proposed framework using an experiment. The results from the experiment show that the framework has the potential to effectively prioritize exceptions in practice.

Keywords: Continuous Auditing, Exception Prioritization, Belief Functions, Belief Revision, Back Propagation, Rule Learner

JEL Classification: M41