materi fraud - ppt bns
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Fraud Examination, 3E
Chapter 6: Data-Driven Fraud
Detection
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Learning Objectives• Describe the importance of data-
driven fraud detection, including thedierence between accountinganomalies and fraud.
• Explain the steps in the data analsisprocess.
• !e familiar with common data
analsis pac"ages.• #nderstand the principles of data
access, including open databaseconnectivit $OD!%&, text import, anddata warehousing.#
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Learning Objectives• 'erform basic data analsis
procedures for fraud detection.
• (ead and anal)e a *atasosmatrix.
• #nderstand how fraud is detectedb anal)ing +nancial statements.
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Data-Driven raud Detection#sing database ueries and other
methods to determine if thosefrauds ma actuall exist.
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Data-Driven raud DetectionFraud v( .noma/ie(
nomalies/
– are not intentional
–
will be found throughout a data set
raud/
–
is intentional – is found in ver few data sets
– is li"e 0+nding a needle in ahastac"1
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2he Data nalsis 'rocessProactive 1 2ot Reactive
• brainstorm the schemes and
smptoms• reuires reengineered methods to
be eective
•
learn new methodologies, softwaretools, and analsis techniues
• a hpothesis-testing approach
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'roactive *ethod of raudDetection
!he &ix &tep( o) the Proactiveethod:
3.#nderstand the business
4.5dentif 'ossible rauds 2hat %ouldExist
6.%atalog 'ossible raud 7mptoms
8.#se 2echnolog to 9ather Databout 7mptoms
:.nal)e (esults
;.5nvestigate 7mptoms4
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#nderstanding the !usiness
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5dentif 'ossible raudsDivide the business into individual
functions
5nterview people in the businessfunctions>as" uestions li"e/
–
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5dentif 'ossible rauds –
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%atalog 'ossible raud7mptoms
Divided into +ve groups $%hapter :&=
– ccounting anomalies
– 5nternal control wea"nesses
– naltical anomalies
– Extravagant lifestles
– #nusual behaviors
– 2ips and complaints
Examp/e: Aic"bac"s
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%atalog 'ossible raud7mptoms
Red F/a*( o) 7ic89ac8(
.na/tica/ &mptom(
• 5ncreasing prices
• Larger order uantities
• 5ncreasing purchases from favoredvendor
• Decreasing purchases from othervendors
• Decreasing ualit
#
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%atalog 'ossible raud7mptoms
Red F/a*( o) 7ic89ac8(
;ehaviora/ &mptom(
•
!uer doesnCt relate well to otherbuers and vendors
• !uerCs wor" habits change
unexpectedl
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%atalog 'ossible raud7mptoms
Red F/a*( o) 7ic89ac8(
+i)e(t/e &mptom(
•
!uer lives beond "nown salar• !uer purchases more expensive
automobile
• !uer builds more expensivehome
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%atalog 'ossible raud7mptoms
Red F/a*( o) 7ic89ac8(
Contro/ &mptom(
• ll transactions with one buer andone vendor
• #se of unapproved vendors
Document &mptom(
• 3s from vendor to buerCs relative
0
l ibl d
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%atalog 'ossible raud7mptoms
Red F/a*( o) 7ic89ac8(
!ip( and Comp/aint(
•
nonmous complaints aboutbuer or vendor
• #nsuccessful vendor complaints
• Fualit complaints aboutpurchased products
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Data nalsis 7oftware.udit Command +an*ua*e ( IDE.
– 'owerful program for data analsis withmore
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Data nalsis 7oftwareicro(o)t O?ce @ .ctiveData
– a plug-in for *icrosoft OGce
– provides data analsis procedures
– based in Excel and ccess
– less expensive alternative to %L and5DE
&.& and &P&&
– 7tatistical analsis programs withavailable fraud modules
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Data ccess9athering the right data in the right
format during the right timeperiod.
*ethods include=
• Open Database %onnectivit$OD!%&
•
2ext 5mport• @osting a Data
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Data ccessOpen Data9a(e Connectivit
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Data ccess!ext Import
– 5mport data with a delimited text$%7H or 27H&
• %7H=
5D, Date, irst Iame, Last Iame, 'honeIumber, etc.
684, 34J46J4K, 7eth, Anab, --,etc.
• 27H=
5D Date irst Iame Last Iame'hone
68434J46J4K 7eth Anab --
– 5mport data with *L or other
language#
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Data ccesso(tin* a Data 'arehou(e
• Data are imported, stored, andanal)ed within %L or otherprogram
• n all-in-one solution for theinvestigator
##
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Data nalsisnalsis techniues that are most
commonl used b fraudinvestigators=
• Data 'reparation
• Digital nalsis
• Outlier 5nvestigation
•
7trati+cation and 7ummari)ation• 2ime 2rend nalsis
• u)) *atching
• !enfordCs Law#3
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Data nalsis M *atasos *atrixOne wa to view the results of
multiple indicators is to use achart called a ata(o( matrix:
#
Contract
'innin*Aendor
2um9er o)Red
F/a*(
+o(t;id(
;rand2ame(
+a(t;idder'inner
&eBuentia/ ;id
&ecurit2um9er
3443Direct
%orp.
3 N KN N N
:46664 7atoo 4 N ;N N 3N
6:3446 Danicorp 3 N K4N N N
6K:86 #nder 5nc. 6 N KN 3N 3N
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inancial 7tatement nalsispproaches to inancial 7tatement
nalsis=
• comparing account balances fromone period to the next
• calculating "e ratios andcomparing them from period toperiod
• performing hori)ontal analsis
• performing vertical analsis
#0