fighting accounting fraud through forensic analytics

144 Pages·2017·1.23 MB·English

DOCTORAL THESIS
F A F
IGHTING CCOUNTING RAUD
T F A
HROUGH ORENSIC NALYTICS
Author: Supervisor:
Maria JOFRE Prof. Richard GERLACH
Dr. Marcel SCHARTH
Athesissubmittedinfulfilmentoftherequirements
forthedegreeofDoctorofPhilosophy
inthe
DisciplineofBusinessAnalytics
BusinessSchool
TheUniversityofSydney
July2017
Declaration of Authorship
I, Maria JOFRE, declare that this thesis, titled “Fighting Accounting Fraud Through
Forensic Analytics” and the work presented in it are my own. This thesis has not
been submitted for any degree or other qualification at this University or any other
institution.
I confirm that the intellectual content of this thesis is the product of my own work,
that I have quoted all published work of others and that I have acknowledged all
mainsourcesofhelp.
MariaJofre
i
Veryfewofthecommonpeoplerealisethat
thepoliticalandlegalsystems
havebeencorruptedbydecades
ofcorporatelobbying.
StevenMagee
ii
Abstract
Accounting Fraud is one of the most harmful financial crimes as it often results in
massive corporate collapses, commonly silenced by powerful high-status executives
and managers. Accounting fraud represents a significant threat to the financial
system stability due to the resulting diminishing of the market confidence and trust
of regulatory authorities. Its catastrophic consequences expose how vulnerable and
unprotectedthecommunityisinregardstothismatter,sincemostdamageisinflicted
toinvestors,employees,customersandgovernment.
Accounting fraud is defined as the calculated misrepresentation of the financial
statement information disclosed by a company in order to mislead stakeholders
regarding the firm?s true financial position. Different fraudulent tricks can be used
tocommitaccountingfraud,eitherdirectmanipulationoffinancialitemsorcreative
methods of accounting, hence the need for non-static regulatory interventions that
take into account different fraudulent patterns. Accordingly, this study aims to
identifysignsofaccountingfraudoccurrencetobeusedto,first,identifycompanies
that are more likely to be manipulating financial statement reports, and second,
assistthetaskofexaminationwithintheriskierfirmsbyevaluatingrelevantfinancial
red-flags,astoefficientlyrecogniseirregularaccountingmalpractices.Toachievethis,
a thorough forensic data analytic approach is proposed that includes all pertinent
stepsofadata-drivenmethodology.
First, data collection and preparation is required to present pertinent information
related to fraud offences and financial statements. The compiled sample of known
fraudulent companies is identified considering all Accounting Series Releases and
Accounting and Auditing Enforcement Releases issued by the U.S. Securities and
Exchange Commission between 1990 and 2012, procedure that resulted in 1,594
fraud-year observations. Then, an in-depth financial ratio analysis is performed
in order to evaluate publicly available financial statement data and to preserve
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only meaningful predictors of accounting fraud. In particular, two commonly used
statistical approaches, including non-parametric hypothesis testing and correlation
analysis, are proposed to assess significant differences between corrupted and
genuine reports as well as to identify associations between the considered ratios.
The selection of a smaller subset of explanatory variables is later reinforced by the
implementationofacompletesubsetlogisticregressionmethodology.
Finally,statisticalmodellingoffraudulentandnon-fraudulentinstancesisperformed
by implementing several machine learning methods. Classical classifiers are
considered first as benchmark frameworks, including logistic regression and
discriminant analysis. More complex techniques are implemented next based on
decision trees bagging and boosting, including bagged trees, AdaBoost and random
forests.
Ingeneral,itcanbesaidthataclearenhancementintheunderstandingofthefraud
phenomenon is achieved by the implementation of financial ratio analysis, mainly
due to the interesting exposure of distinctive characteristics of falsified reporting
and the selection of meaningful ratios as predictors of accounting fraud, later
validatedusingacombinationoflogisticregressionmodels. Interestingly,usingonly
significantexplanatoryvariablesleadstosimilarresultsobtainedwhennoselectionis
performed. Furthermore, better performance is accomplished in some cases, which
strongly evidences the convenience of employing less but significant information
whendetectingaccountingfraudoffences.
Moreover,out-of-sampleresultssuggestthereisagreatpotentialindetectingfalsified
accounting records through statistical modelling and analysis of publicly available
accountinginformation. Ithasbeenshowngoodperformanceofclassicmodelsused
as benchmark and better performance of more advanced methods, which supports
the usefulness of machine learning models as they appropriately meet the criteria
of accuracy, interpretability and cost-efficiency required for a successful detection
methodology.
This study contributes in the improvement of accounting fraud detection in
several ways, including the collection of a comprehensive sample of fraud and
non-fraudfirmsconcerningallfinancialindustries,anextensiveanalysisoffinancial
information and significant differences between genuine and fraudulent reporting,
selection of relevant predictors of accounting fraud, contingent analytical modelling
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for better differentiate between non-fraud and fraud cases, and identification of
industry-specificindicatorsoffalsifiedrecords.
The proposed methodology can be easily used by public auditors and regulatory
agencies in order to assess the likelihood of accounting fraud and to be adopted in
combinationwiththeexperienceandinstinctofexpertstoleadtobetterexamination
ofaccountingreports. Inaddition,theproposedmethodologicalframeworkcouldbe
of assistance to many other interested parties, such as investors, creditors, financial
and economic analysts, the stock exchange, law firms and to the banking system,
amongstothers.
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Acknowledgements
Firstofall,Iwouldliketothankmysupervisoryteamwhohaspatientlyguidedme
duringthisfascinatingjourney. AbigthankyoutomymainsupervisorProf. Richard
Gerlachandtomyco-supervisorDr. MarcelScharthfortheirconstantsupport. They
bothhavecontributedsignificantlytomyresearchandprofessionalachievements. I
wouldliketoextendmyacknowledgetoDr. DemetrisChristodoulouwhosupported
me during the first years of the program and helped me with all administrative
processes and other issues. I am also indebted to the thesis examiners who have
made meaningful corrections and suggestions, which have improved the quality of
theworkpresentedinthisdocument.
Special thanks to Conicyt, institution responsible for providing financial support to
Chilean students, as without the provided scholarship (Becas Chile) it would have
beenverydifficulttoaffordmystudiesandlivingexpensesduringthis4-yearperiod.
Inaddition,IwouldliketothanktheSecuritiesClassActionClearinghouse,Stanford
Law School, for providing access to a very comprehensive database of accounting
fraudcases.
Most importantly, a huge thanks to my mom, dad and brother, who have given
me unconditional love and support throughout my life. Especially grateful to my
grandmother Maria Elizabeth, who lights candles for every little PhD-related event.
Also, absolutely thankful to my late grandmother Alicia Berta de Jesus (R.I.P.), to
whomIwouldlovetohugandsharealaugh,ormaybeatearortwo.
Always grateful to all my friends who have had an influence on my life in some
unique and magical way. I cannot express enough how eternally thankful I am to
sharemylifewithsuchexceptionalbeings.
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Contents
DeclarationofAuthorship i
Abstract iii
Acknowledgements vi
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 ResearchApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 ThesisOutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 TheAccountingFraudPhenomenon 7
2.1 OverviewofWhite-CollarCrime . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 OverviewofCorporateCrime . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 OverviewofAccountingFraud . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 InfamousAccountingScandals . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5 Victims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.6 ForensicAccounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.7 LiteratureReview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.8 ProposedMethodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 ForensicDataAnalysis 26
3.1 ForensicAnalytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 FraudDataCollection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3 FinancialDataCollection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1 BalanceSheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.2 IncomeStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
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3.3.3 CashFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 DataPreparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4.1 DataCleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.2 DataTransformation . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.3 DataMerging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.4 DataValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.5 MissingValues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5 SampleSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.6 ExploratoryDescriptiveAnalysis . . . . . . . . . . . . . . . . . . . . . . . 37
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 FinancialRatioAnalysis 42
4.1 FinancialRatios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 OutlierDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3 RatioAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4 RatioAnalysisbyIndustry . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.4.1 StandardIndustrialClassificationOverview . . . . . . . . . . . . 60
4.4.2 AnalysisbyIndustry . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.5 CorrelationAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5.1 PearsonCorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5.2 KendallCorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.6 VariableSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5 CompleteSubsetLogisticRegression 73
5.1 TheoreticalBackground . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2 CompleteSubsetLogisticRegression . . . . . . . . . . . . . . . . . . . . . 75
5.3 ModellingAssessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6 AccountingFraudModelling 90
6.1 StatisticalModelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.2 MachineLearningMethods . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.2.1 DiscriminantAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2.2 LogisticRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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6.2.3 AdaBoost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2.4 DecisionTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.2.5 BoostedTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.2.6 RandomForests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7 FinancialIndicatorsofAccountingFraud 111
7.1 MiningandConstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2 Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.3 Transportation,Communication,Electric,GasandSanitaryService . . . 113
7.4 WholesaleTradeandRetailTrade . . . . . . . . . . . . . . . . . . . . . . . 115
7.5 Finance,InsuranceandRealEstate . . . . . . . . . . . . . . . . . . . . . . 117
7.6 Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
7.7 PublicAdministration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
8 Conclusions,LimitationsandFutureWork 120
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
8.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
8.3 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Bibliography 124
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