An Evidential Reasoning Approach to Sarbanes-Oxley Mandated Internal Control Risk Assessment

This is the peer reviewed version of the following article: Mock, T., L. Sun, R. P. Srivastava, and M. Vasarhelyi. " An Evidential Reasoning Approach to Sarbanes-Oxley Mandated Internal Control Risk Assessment under Dempster-Shafer Theory", 2009, ABACUS, Vol. 45, No. 1, pp. 66-87. , which has been published in final form at http://doi.org/10.1016/j.accinf.2008.10.003. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In response to the enactment of the Sarbanes-Oxley Act 2002 and of the release of the Public Company Accounting Oversight Board (PCAOB) Auditing Standard No. 5, this study develops a risk-based evidential reasoning approach for assessing the effectiveness of internal controls over financial reporting (ICoFR). This approach provides a structured methodology for assessing the effectiveness of ICoFR by considering relevant factors and their interrelationships. The Dempster-Shafer theory of belief functions is utilized for representing risk. First, we develop a generic ICoFR assessment model based upon a Big 4 audit firm’s approach and apply it to a real-world example. Then, based on this model, we develop a quantitative representation of various levels of ICoFR effectiveness and related risk-assessment as defined by the PCAOB and contrast these representations with levels implied by Auditing Standard No. 5. In doing so, we demonstrate the potential value of formal risk assessment models in both facilitating the assessment of risks in an individual engagement and in assessing the effects of different regulations

AUDITOR\u27S ASSISTANT: A knowledge engineering tool for audit decisions

https://egrove.olemiss.edu/dl_proceedings/1039/thumbnail.jp

Accuracy, Certainty and Surprise - A Prediction Market on the Outcome of the 2002 FIFA World Cup

In this chapter, we present our empirical investigation of the forecasting accuracy of a prediction market experiment drawn on the outcome of the World Cup 2002. We analyse the predictive accuracy of 64 markets and compare to bookmakers’ quotes and chance as benchmarks. We revisit the evaluation of Schmidt and Werwatz (Chapter 16) and compare our results directly to their findings. In addition, we propose a new method for testing predictive accuracy by means of a non-parametric test for the similarity of probability distributions and we evaluate the incorporation of information in market prices by comparing pre-match and half-time price data. We find a reversed favourite-longshot bias when analysing market prices before the start of the match and this bias does not disappear with the inflow of new information until half-time. Unlike the market based predictions bookmakers appear to be perfectly calibrated. Since there were substantial deviations in outcome between the 2000 European Championship and our data, we offer possible explanations for the much worse performance of the 2002 World Cup prediction market. Consistent with Schmidt and Werwatz (Chapter 16) prediction markets do assign relatively higher probabilities to the favourite when compared to the odds-setters. Together with a long streak of surprising outcomes this fact appears most likely to be responsible for the predictive inaccuracy.

Applications of Belief Functions in Business Decisions: A Review

This is the author's final draft. The publisher's official version is available from: .In this paper, we review recent applications of Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in the application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension of DST, can provide an alternative solution to a wide range of business problems

Structural Analysis of Audit Evidence Using Belief Functions

This article performs two types of analysis using Dempster-Shafer theory of belief functions for evidential reasoning. The first analysis deals with the impact of the structure of audit evidence on the overall belief at each variable in the network, variables being the account balance to be audited, the related transaction streams, and the associated audit objectives. The second analysis deals with the impact of the relationship (logical "and" and "algebraic relationship") among various variables in the network on the overall belief. For our first analysis, we change the evidential structure from a network to a tree and determine its impact

Enterprise Risk Management: Review, Critique, and Research Directions

© 2014 Elsevier Ltd. Many regulators, rating agencies, executives and academics have advocated a new approach to risk management: Enterprise Risk Management (ERM). ERM proposes the integrated management of all the risks an organization faces, which inherently requires alignment of risk management with corporate governance and strategy. Academic research on ERM is still in its infancy, with articles largely in accounting and finance journals but rarely in management journals. We argue that ERM offers an important new research domain for management scholars. A critical review of ERM research allows us to identify limitations and gaps that management scholars are best equipped to address. This paper not only identifies how management scholars can contribute to ERM research, but also points out why ERM research (and practice) needs management research for its development

An Evidential Reasoning Approach to Fraud Risk Assessment under Dempster-Shafer Theory: A General Framework

This paper develops a general framework under Dempster-Shafer theory for assessing fraud risk in a financial statement audit by integrating the evidence pertaining to the presence of fraud triangle factors (incentives, attitude and opportunities), and evidence concerning both account-based and evidence-based fraud schemes. This framework extends fraud risk assessment models in prior research in three respects. 1) It integrates fraud schemes, both account schemes through which accounts are manipulated, and evidence schemes through which frauds are concealed, into a single framework. 2) It incorporates prior fraud frequency information obtained from the Accounting and Auditing Enforcement Releases issued by the Securities and Exchange Commission into an evidential network which uses Conditional OR relationships among assertions. 3) The framework provides a structured approach for connecting risk assessment, audit planning, and evaluation of audit results. The paper uses a real fraud case to illustrate the application of the framework

An Expert System Approach to Audit Planning and Evaluation in the Belief-Function Framework

This is the author's final draft. The publisher's version is available from:

The Belief-Function Approach to Aggregating Audit Evidence

This is the peer reviewed version of the following article: Srivastava, R. P., "The Belief-Function Approach to Aggregating Audit Evidence" International Journal of Intelligent Systems, Vol. 10, No. 3, March 1995, pp. 329-356., which has been published in final form at http://doi.org/10.1002/int.4550100304. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this article, we present the belief-function approach to aggregating audit evidence. The approach uses an evidential network to represent the structure of audit evidence. In turn, it allows us to treat all types of dependencies and relationships among accounts and items of evidence, and thus the approach should help the auditor conduct an efficient and effective audit. Aggregation of evidence is equivalent to propagation of beliefs in an evidential network. The paper describes in detail the three major steps involved in the propagation process. The first step deals with drawing the evidential network representing the connections among variables and items of evidence, based on the experience and judgment of the auditor. We then use the evidential network to determine the clusters of variables over which we have belief functions. The second step deals with constructing a Markov tree from the clusters of variables determined in step one. The third step deals with the propagation of belief functions in the Markov tree. We use a moderately complex example to illustrate the details of the aggregation process