Can Artificial Intelligence Detect Biased Client Statements to Improve the Auditor-Client Inquiry Process?

Abstract

Prior research has identified extensive limitations in the auditor-client inquiry process. For example, clients can craft inquiry responses to persuade the auditor to accept the client’s aggressive accounting position and research shows that auditors are susceptible to such persuasion attempts. Additionally, research shows that auditors have difficulty identifying deception in client responses and are faced with challenges related to the sheer number of transactions present in modern global enterprises. To address these concerns, we are developing an innovative automated inquiry system that relies on natural language processing and machine learning to evaluate client responses to automated inquiry. In this paper, we follow a design science approach to develop and test the key artifact that learns from and evaluates a set of communication data generated by students who took the roles of auditors and clients. These participants were paired in dyads who communicated over email to discuss a potential inventory obsolescence issue. The clients were randomly assigned to either an aggressive reporter condition (i.e., they aim to report income as high as possible) or an accurate reporter condition (i.e., they aim to report income as accurately as possible). Using a subset of the data, the system learns to identify systematic differences between aggressive and accurate reporters and then evaluates the remaining data to identify whether the clients are aggressive versus accurate reporters. We find that the system properly classifies the clients at a rate greater than chance, demonstrating the feasibility of the technology. Additionally, the system’s learning approach outperforms text analysis using LIWC software which ascribes advance meaning to the words. As audit firms invest heavily in automation, this study has important practical contribution to enhance auditors’ ability to audit enormous, highly complex global companies.