In this paper we model the interaction between an auditor and a client firm. The client firm’s manager can either report truthfully or commit fraud. The auditor needs to plan a two stage audit that allows to detect fraud. In the first stage an AI tool is employed that provides a signal about the quality of the client’s internal control system (ICS). Classifying the ICS as weak or strong, the signal alters the auditor’s expectations regarding the client’s fraud probability. In the second stage, the auditor decides about her audit effort conditional on the information provided by the AI. Comparing the AI setting to a benchmark setting without AI use, we find that employing the AI tool reduces the manager’s incentives to commit fraud. At the same time it reduces the equilibrium effort provided by the auditor. As a consequence, the probability that actual fraud is detected remains unchanged. We extend our model and allow the AI tool to be customized such that it can either focus on detection of the weak ICS, the strong ICS, or on both equally. We find that the AI specification that minimizes ex ante probability for fraud not necessarily coincides with the specification that minimizes auditing costs. It follows that the auditor in charge of customizing the AI cannot necessarily be expected to do so in a fraud minimizing way.
Schöndube et al. (Wed,) studied this question.