Supporting a complex audit judgment task: An expert network approach

An auditor considers a tremendous amount of data when assessing the risk that the internal control (IC) structure of an entity will fail to prevent or detect significant misstatements in financial statements. The myriad of relationships between IC variables that must be identified, selected, and analyzed often makes assessing control risk a difficult task. While some general procedures and guidelines are provided, audit standards dictate no specifically set procedures and rules for making a preliminary control risk assessment (CRA). Rather, the procedures and rules are left mostly to auditor judgment. This paper considers the appropriateness of applying artificial intelligence (AI) techniques to support this audit judgment task. It details the construction of a prototype expert network; an integration of an expert system (ES) and a neural network (NN). The rules contained in the ES model basic CRA heuristics, thus allowing for efficient use of well-known control variable relationships. The NN provides a way to recognize patterns in the large number of control variable inter-relationships that even experienced auditors cannot express as a logical set of specific rules. The NN was trained using actual case decisions of practicing auditors.