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:

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

Representation of Interrelationships among Binary Variables under Dempster-Shafer Theory of Belief Functions

This is the peer reviewed version of the following article: Srivastava, R. P., L. Gao, and P. Gillett. " Representation of Interrelationships among Binary Variables under Dempster-Shafer Theory of Belief Functions" (pre-publication version), 2009, International Journal of Intelligent Systems, Volume 24 Issue 4, pp. 459 - 475, which has been published in final form at http://doi.org/10.1002/int.20347. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This paper presents an algorithm for developing models under Dempster-Shafer theory of belief functions for categorical and 'uncertain' logical relationships among binary variables. We illustrate the use of the algorithm by developing belief-function representations of the following categorical relationships: 'AND', 'OR', 'Exclusive OR (EOR)' and 'Not Exclusive OR (NEOR)', and 'AND-NEOR' and of the following uncertain relationships: 'Discounted AND', 'Conditional OR', and 'Weighted Average'. Such representations are needed to fully model and analyze a problem with a network of interrelated variables under Dempster-Shafer theory of belief functions. In addition, we compare our belief-function representation of the 'Weighted Average' relationship with the 'Weighted Average' representation developed and used by Shenoy and Shenoy8. We find that Shenoy and Shenoy representation of the weighted average relationship is an approximation and yields significantly different values under certain conditions

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

Decision Making Intelligent Agent on SOX Compliance over the Imports Process

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Imports Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Imports Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

Evidential Reasoning for WebTrust Assurance Services

This is the author's final draft. The publisher's official version is available from: http://www.jmis-web.orgThis study looks at two aspects of assurance services. The first deals with the type(s) of evidential networks that will allow a professional accountant to provide assurance. Here, we develop an evidential network model for “WebTrust Assurance,” a service being provided by the American Institute of Certified Public Accountants (AICPA) and the Canadian Institute of Chartered Accountants (CICA). Our model augments the AICPA/CICA approach and provides goals, sub-goals and evidence relevant to the overall assurance to be provided. The aggregation of evidence and the resolution of uncertainties follow the belief-function approach of Srivastava and Shafer. Next we develop a decision theoretic model for the assurance-planning problem. Our approach is based on estimating the expected value of providing various levels of assurance and is illustrated with several different scenarios that may be faced in practice. We also consider the role of ambiguity in decision situations such as planning WebTrust engagements and calculate bounds in expected value based on whether auditors are conservative or not in their approach to risk

Decision Making Intelligent Agent on SOX Compliance over the Goods Receipt Processs

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Goods Receipt Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Goods Receipt Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

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

The Belief-Function Approach to Aggregating Audit Evidence

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