Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors

Abstract

We develop an analytical model intended as the first stage in the development of expert systems to improve auditor knowledge in, and assist in the decision process of, Going Concern Opinions (“GCOs”). Our approach is consistent with a design science approach to developing information systems, resulting in an initial artifact, the mathematical model, which can, through iterative design science and behavioral research, inform a technology-based expert system. Based on Bayesian networks, our model provides insights about auditors’ revision, or inflation, of the probability to issue a GCO based on the interrelationship that forms with the incremental existence of one, two, or three publicly observable financial statement risk factors – net operating loss, negative cash flows from operations, and negative working capital. We calculate the revised probabilities using empirical data of GCOs from 2004 to 2015. Results reveal that the incremental relationship (one, two, or three factors present) effectively models expert auditors’ decisions to issue a GCO, and suggests the existence of these measurable inflation factors that represent situational and auditor-specific factors. We also find that Non-Big Four auditors inflate these factors differently than Big Four auditors to arrive at a decision to issue a GCO.