Auditor Risk Assessment: Insights from the Academic Literature

2006 ◽  
Vol 20 (2) ◽  
pp. 157-177 ◽  
Author(s):  
Robert D. Allen ◽  
Dana R. Hermanson ◽  
Thomas M. Kozloski ◽  
Robert J. Ramsay

To contribute to the PCAOB project on risk assessment in financial statement audits, we draw on the academic literature to offer insights and conclusions on the risk-assessment process. We use the PCAOB's (2005) recent briefing paper on risk assessment as the organizing framework for our literature review, and we examine academic auditing literature addressing topics including business risk, inherent risk, control risk, fraud risk, linking risk assessments to subsequent testing, and the audit risk model. Overall, we believe that the results of academic research are consistent with the PCAOB staff's apparent reconsideration of the auditor's risk-assessment process. We conclude with identification of future research topics and recognition of barriers to performing research that is relevant to standard setters.

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 289
Author(s):  
Maria Krechowicz ◽  
Adam Krechowicz

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions.


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