The Effect of Alternative Business Model Representation Techniques on Business and Audit Risk Assessment

We investigate the effects of business model representation techniques, specifically format and presence of causal linkages, on business risk and audit risk assessment. We conduct an experiment involving auditing students with previous audit work experience as participants and business model information based on an existing public company. Participants given business model representation in either diagrammatical or tabular format make more accurate risk assessments than those given the same information in a free-form narrative format. Contrary to our prediction, overall performance in diagram and table conditions does not differ statistically. The inclusion of causal linkages in the business model representation has mixed effects on risk assessment accuracy. We also investigate whether task-specific experience moderates the effects of representation techniques on risk assessment. We find an interaction effect of task-specific experience with causal linkages; specifically, linkage effects are limited to the subsample of participants with no risk documentation experience

Computer-Assisted Functions for Auditing XBRL-Related Documents

The increasing global adoption of XBRL and its potential to replace traditional formats for business reporting raise questions about the quality of XBRL-tagged information. In this paper, we identify a set of issues and audit objectives that auditors might confront if they are asked to provide assurance procedures on the XBRL-related documents. We also address useful computer-assisted audit functions for supporting various audit tasks on XBRL instance documents and extension taxonomies and discuss how the identified audit objectives could be accomplished using these functions

Adaptive Fraud Detection using Benford’s Law

Abstract. Adaptive Benford’s Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for incomplete data records. We combine this digital analysis technique with a reinforcement learning technique to create a new fraud discovery approach. When applied to records of naturally occurring phenomena, our adaptive fraud detection method uses deviations from the expected Benford’s Law distributions as an indicators of anomalous behaviour that are strong indicators of fraud. Through the exploration component of our reinforcement learning method we search for the underlying attributes producing the anomalous behaviour. In a blind test of our approach, using real health and auto insurance data, our Adaptive Fraud Detection method successfully identified actual fraudsters among the test data.

The Impact of Executive Team Competencies on XBRL Aoption

The issue of determinants of a search-facilitating technology such as “Extended Business Reporting Language (XBRL)” has drawn considerable attention from the global academic community. This research focuses on executive team characteristics to investigate their association with the voluntary adoption of XBRL technology beyond the effect of firm characteristics. We investigated whether these characteristics (information system- and/or business/financial related- competencies) within the executive team affected the quality of the XBRL-tagged filings. Our findings demonstrate higher levels of information systems competencies were positively associated with early adoption of XBRL; whereas, higher levels of other business related-competencies (e.g. financial expertise) were negatively associated with it. Furthermore, IS competency was negatively associated with the technical aspects of XBRL. These results extend the literature on the influence of management on corporate decisions and can be used as a guide for investigating voluntary adoption of other reporting technologies, and further inform regulators and users of XBRL