Exception prioritization in the continuous auditing environment: A framework and experimental evaluation

Researchers have found that the volume of exceptions generated by a continuous auditing system can be overwhelming for an internal audit department to handle. In this paper, we propose and validate a framework that systematically prioritizes exceptions based on the likelihood of an exception being erroneous or fraudulent and evaluate the framework using an experiment. The framework consists of six stages: (1) generation of exceptions using defined rules, (2) assignment of suspicion scores to exceptions using belief functions, (3) exception prioritization, (4) exception investigation, (5) rule confidence level update utilizing back propagation, and (6) rule(s) addition utilizing a rule learner algorithm. We also simulate the proposed framework using an experiment. The experiment results provide evidence that the framework can be effective in prioritizing exceptions and thus maximizing audit efficiency.