materi fraud - ppt bns

8/20/2019 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.

3


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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

0


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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

6


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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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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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;ehaviora/ &mptom(

•

!uer doesnCt relate well to otherbuers and vendors

• !uerCs wor" habits change

unexpectedl

3


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+i)e(t/e &mptom(

•

!uer lives beond "nown salar• !uer purchases more expensive

automobile

• !uer builds more expensivehome


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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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!ip( and Comp/aint(

•

nonmous complaints aboutbuer or vendor

• #nsuccessful vendor complaints

• Fualit complaints aboutpurchased products

6


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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

5


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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

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