Text

theses

The increasing utilization of computerized systems in businesses has led to the generation and storage of massive databases. In light of the availability of such big data, auditing is moving from the traditional sample-based approach to audit-by-exception. The literature is abundant with studies that propose various machine learning, statistical, and data mining techniques that have proved to be efficient in identifying exceptions. However, such techniques often inundate auditors and management with large numbers of exceptions. This dissertation, composed of three essays, attempts to help them overcome the human limitations of dealing with information overload by proposing methodologies to detect and subsequently prioritize such exceptions. These prioritization techniques can help auditors and management to direct their investigations towards the more suspicious cases, or exceptional exceptions. The first essay evaluates the quality of auditors’ judgment of business processes’ risk levels using historic data procured from internal controls risk assessments of a multinational company. I identify the exceptions where auditor assessments deviate from the value predicted by an ordered logistic regression model. Subsequently, I propose two metrics to prioritize these exceptions. The results indicate that the prioritization methodology proved effective in helping auditors focus their efforts on the more problematic audits. In the second essay I propose a framework where I use a weighted rule-based expert system to identify exceptions that violate internal controls. These exceptions are then prioritized based on a suspicion score, defined as the sum of the risk weightings of all the internal controls that were violated by that specific record. Finally, the exceptions are ranked by decreasing order of suspicion score. The third essay addresses the problem of data quality from a duplicate records perspective. I present the various techniques used to detect such duplicates, and focus on the issue of duplicate payments. I use two real business datasets as an illustration. Finally I propose a prioritization methodology where each duplicate candidate receives a cumulative score based on multiple criteria. The results show that my prioritization methodology can help the auditors to process duplicate candidates more effectively.

ETD_4972

Ph.D.

Includes bibliographical references

Includes vita

by Hussein Issa

doi:10.7282/T32J68V1

ETD doctoral

The author owns the copyright to this work.