Journal Entry Complexity Measurement and Anomaly Detection

ObjectiveAccording to the International Standards on Auditing (ISA), financial transaction complexity, an inherent fraud risk factor, stands among the criteria for selecting accounting journal entries to test, and implement analytical procedures for anomaly detection to assess fraud risk. However, the extant academic and professional literature lacks a structural definition of accounting journal entry complexity. This study aims to fill this gap by (1) proposing a novel quantitative measure of journal entry complexity and (2) applying it to anomaly detection techniques to identify and assess the risks of material misstatement.MethodsGiven the purpose of this study, the Design Science (DS) methodology (Hevner et al., 2004) was adopted. The DS includes two phases: artifact design and evaluation. In the design phase, a content analysis of ISA and a literature review of complexity and diversity were conducted to establish the basis for defining journal entry complexity. Subsequently, the proposed measure was adapted from diversity indices used in the biological sciences to meet the specific requirements of the research problem. This adjustment incorporated innovations from both exaptations and improvements in the contributions of DS. In the evaluation phase, descriptive and observational approaches were employed to assess and verify the novelty and utility of the proposed artifact.ResultsIn the absence of an explicit definition of transaction complexity in auditing standards and guidelines, the content analysis of ISA led to the extraction of five conceptual dimensions of complexity: (1) the number and relationships of components, (2) the nature and form of transactions, (3) measurement and processing of information, (4) quantity and quality of knowledge, and (5) the degree of change and uncertainty regarding the subject matter. Based on the first dimension of this conceptualization and its adaptation to the theoretical foundations of diversity in biological sciences, the journal entry complexity measure was defined from a structural and data-driven perspective, as a function of the number and diversity of accounts involved. Next, by adapting the biodiversity index (Clarke & Warwick, 1998) and adopting the taxonomic distance measure based on the path length to determine account distances, a quantitative measure of journal entry complexity, as a design science artifact of the model type, was provided. The measure was then applied to detect global and contextual anomalies in journal entries. The implementation and evaluation phases continued through a case study using the Python programming language for analyzing journal entry complexity to identify global and size and pattern-based contextual anomalies in 2,895 journal entries of a manufacturing company. The results and insights obtained from applying the measure were then discussed and evaluated.ConclusionAdopting an interdisciplinary approach, this study applies theoretical foundations and biodiversity measurement methods from biological sciences to create a systematic and flexible mechanism for measuring the complexity of journal entries and identifying anomalies. It seeks to improve the identification and assessment of material misstatement risks in audit analytical procedures. Moreover, using this measure helps in planning and optimizing audit resource allocation by accounting for the complexity level of audit engagements. It also improves audit sampling and prioritizes auditing journal entries based on their complexity, as an inherent risk factor.