Developing Quantitative Models for Auditing Journal Entries

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Title: Developing Quantitative Models for Auditing Journal Entries
Author: Argyrou, Argyris
Contributor: Hanken School of Economics, Department of Accounting and Commercial Law, Accounting
Belongs to series: Economics and Society – 255
ISSN: 2242-699X
ISBN: 978-952-232-195-4
Abstract: The thesis examines how the auditing of journal entries can detect and prevent financial statement fraud. Financial statement fraud occurs when an intentional act causes financial statements to be materially misstated. Although it is not a new phenomenon, financial statement fraud has attracted much publicity in the wake of numerous cases of financial malfeasance (e.g. ENRON, WorldCom). Existing literature has provided limited empirical evidence on the link between auditing journal entries and financial statement fraud. The lack of evidence contrasts sharply with the responsibility of auditors to test the appropriateness of journal entries recorded in a general ledger. It becomes more pronounced when considering that journal entries pose a high risk of financial statement fraud, as the case of WorldCom has demonstrated. It is further exacerbated given that fraud results in considerable costs to a number of parties, for example: auditors may be exposed to litigation; investors may experience negative stock returns; and, capital markets may suffer from reduced liquidity. Motivated by these considerations, the thesis adopts the tenets of design-science research in order to develop three quantitative models for auditing journal entries. It first employs self-organizing map and extreme value theory to design the models as constructs. Subsequently, it codes the constructs in MATLAB to build functioning instantiations; and finally, it evaluates the instantiations by conducting a series of experiments on an accounting dataset containing journal entries. The contribution of the thesis lies in the proposed models and their potential applications in accounting. The first model can assist management to monitor the processing of journal entries as well as to assess the accuracy of financial statements. The second model can detect novel journal entries that differ from legitimate journal entries to such an extent that they could be ‘suspicious’. The third model can identify those journal entries that have both a very low probability of occurring and a monetary amount large enough to materially misstate financial statements. The thesis has a novelty value in that it investigates financial statement fraud from the unexplored perspective of journal entries. The thesis can lead to concrete practical applications in accounting, as the models can be implemented as a Computerised Assisted Audit Technique. This potentiality can be the focal point of additional research.
Date: 2013-05-28
Subject: auditing
journal entries
financial statement fraud
self-organizing map
extreme value theory

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