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TRANSCRIPT
Innovation of Fraud Deterrence System
in the Organization Using Forensic
Accounting and Data Mining
Techniques
Pornchai Naruedomkul, Ph.D., CFE
Fraud cases are increasing year on year.
Loss due to fraudulent is about 5–7% of the revenues in the U.S.A.
Fraud: A Critical Issue for Business
Source: Adapted from the ACFE Report to the Nations
1996, 2002, 2004, 2006, 2008, 2010, 2012, and 2014
Estimated Loss to Occupational Fraud
and Abuse in U.S.A.
1995-1996 2002 2004 2006 2008 2010 2011 2013
Gross domestic products 6.67 10.00 11.00 13.04 14.20 58.00 70.00 73.87
Loss to occupational fraud and abuse 0.40 0.60 0.66 0.65 0.99 2.90 3.50 3.70
Loss to gross domestic products (%) 6.00 6.00 6.00 5.00 7.00 5.00 5.00 5.00
DescriptionYear
(Trillion USD)
From KPMG Survey in 2007:
Fraud risk is a major issue.
Fraud issues found in Thailand are similar to those
in other countries.
Fraud cases would increase in the next 2 years.
Fraud in Thailand
Source: Adapted from KMPG, 2007
Amount 2005 2007
Baht 10 million and above 11% 16%
Baht 5 million to less than 10 million 5% 8%
Baht 1 million to less than 5 million 15% 18%
Baht 100,000 to less than 1 million 31% 27%
Less than Baht 100,000 38% 31%
Estimated Financial Losses from
Fraud Detected in Thailand
Fraud Definition
Albrecht et al. (2009: 7) defined fraud as:
“a generic term, and embraces all the multifarious
means which human ingenuity can devise, which are
resorted to by one individual, to get an advantage over
another by false representations. No definite and
invariable rule can be laid down as a general
proposition in defining fraud, as it includes surprise,
trickery, cunning and unfair ways by which another is
cheated. The only boundaries defining it are those
which limit human knavery.”
Fraud Definition
Wells (2010: 8 and 2011: 2) defined fraud as:
“encompass any crime for gain that uses deception
as its principal modus operandi. Of the three ways
to illegally relieve a victim of money — force,
trickery, or larceny — all offenses that employ
trickery are frauds.”
Maslow’s hierarchy of needs
Source: Adapted from Maslow, 1943
Self-actualization
Esteem needs
Love needs
Safety needs
Physiological needs
Possible Causes for Fraud
Theory of planned behavior
Source: Adapted from Ajzen: 182
Possible Causes for Fraud
Fraud Triangle
Source: Adapted from Singleton et al., 2006: 9
Source: Adapted from Wilhelm, 2004
Fraud Deterrence
Fraud Prevention
Fraud Detection
Fraud Mitigation
Fraud Analysis Fraud Policy
Fraud Investigation
Fraud Prosecution
Fraud deterrence will stop fraud before it happens.
Fraud Management Lifecycle Theory
Risk Factors and Red Flags Indicators
Living beyond means
Financial difficulties
Control issues, unwillingness to share duties
Unusually close association with vendor/customer
Wheeler-dealer attitude
Divorce/family problems
Irritability, suspiciousness, or defensiveness
Addiction problems
Source: Adapted from Association of Certified Fraud Examiners Report 1996, 2002, 2004, 2006, 2008 and 2010; Coenen, 2008
Risk Factors and Red Flags Indicators
Refusal to take vacations
Past employment-related problems
Complained about inadequate pay
Excessive pressure from within organization
Past legal problems
Instability in life circumstances
Excessive family/peer pressure for success
Complained about lack of authority
Source: Adapted from Association of Certified Fraud Examiners Report 1996, 2002, 2004, 2006, 2008 and 2010; Coenen, 2008
Forensic Accounting Definition
Bolgna and Linquist (1995) and The Accountant’s
Handbook on Fraud & Commercial Crime defined it as: “the application of financial skills and an investigative mentality to unresolved issues, conducted within the context of the rules of evidence” (cited in Digabriele J. A., 2008: 331; Mehta and Mathur, 2007: 1575).
Hopwood, Leiner, and Young (2009: 3) defined it as: “the application of investigative and analytical skills for the purpose of resolving financial issues in a manner that meets standards required by courts of law.”
Data Mining Definition
Han and Kamber (2001: 5) defined it as “extracting or
‘mining’ knowledge from large amount of data”.
Hand, Mannila, and Smyth (2001: 5) defined it as “the
analysis of (often large) observational data sets to find
unsuspected relationships and to summarize the data in
novel ways that are both understandable and useful to the
data owner”.
Data Mining Technique
Data mining combines many fields into its technique; machine
learning, pattern recognition, statistics, databases and visualization
to execute the unknown information by extraction from large
database (Cabena et al., 1999; Han and Kamber, 2001).
Data Mining Techniques
1. Supervised method
Classification
Prediction
2. Unsupervised method
Association rule
Clustering
Outlier
Data Mining Technique
Classification
The unknown data will be classified into groups based on the similar data
known by the classification to develop the patterns (Han and Kamber, 2001;
Shmueli, Patel, and Bruce, 2007).
Prediction
Prediction is similar to classification except that prediction tries to predict the
value of a numerical variable rather than a class (Han and Kamber, 2001;
Shmueli, Patel, and Bruce, 2007).
Association rule
This technique will be used with patterns which occur frequently in data (Han
and Kamber, 2001). Frequent patterns can be item sets, subsequences, and
substructures; item sets refers to a set of items normally appeared together (i.e.,
a customer normally buys shampoo and conditioner); subsequent refers to the
pattern that a customer has an intention to buy a camera will follow by buying
a memory card. Mining this pattern will lead to a discovery of useful
association of data (Han and Kamber, 2001).
Data Mining Technique
Cluster analysis
The data will be clustered or grouped without consulting a
known class label. “The objects are clustered or grouped based
on the principle of maximizing the intraclass similarity and
minimizing the interclass similarity” (Han and Kamber, 2001:
26).
Outlier analysis
The data objects that are totally different from other groups of
data are considered as outliers. This method “can be used in
fraud detection” (Han and Kamber, 2001: 451) by detecting the
unusual transactions.
Attitude and behavior survey was conducted to collect the information from 2,000 participants.
Cluster Analysis
Cluster algorithms in WEKA* were used to generate the patterns of fraud risk behaviors which would be used as fraud deterrence model.
1. K-means
2. Expectation-Maximization (EM)
3. Filtered cluster
4. Make density-based cluster
*Waikato Environment for Knowledge Analysis (WEKA) implemented by University of Waikato in New Zealand is the leading open-source project in machine learning.
Fraud Deterrence Model
Patterns from cluster algorithms are appropriate for fraud
detection because they classify the similar cases in the
same cluster.
Prediction of potential frauds varies depending on the
behaviors of fraudsters. Fraud risk behaviors may not be
suited with all clusters generated.
Cluster analysis is not appropriate to be used to generate
the patterns or rules for prediction of fraud risk
behaviors.
Fraud Deterrence Model
Association Rule
Association rule is more appropriate to be used to find out the
important rules for fraud potential to occur in association with
each fraud risk factor.
Apriori algorithm in association rule of WEKA program is
selected to be used to find out the best rules.
Association rule is generally expressed in the form of X → Y
or if X then Y
Fraud Deterrence Model
Minimum support of X→Y means the percentage of transactions in the database that contain both X and Y.
Example
Total participants of attitude and
behavior survey = 2,000
Participants who had living beyond means
And financial difficulties (X and Y) = 225
Minimum support (X →Y) = 225/2000
= 0.11
Fraud Deterrence Model
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Minimum confidence of X → Y means the percentage of transaction that contain items in X and also contain items in Y.
Example
Total participants of attitude and
behavior survey = 2,000
Participants who had living beyond means (X) = 275
Participants who had financial difficulties (Y) = 260
Participants who had living beyond means
and financial difficulties (X and Y) = 225
Minimum confidence (X →Y) = 225/275
= 0.82
Fraud Deterrence Model
Copyright by Pornchai Naruedomkul, Ph.D., CFE
The minimum support and the minimum metric (confidence) were set
up in 4 sets as follows:
1. Minimum support = 0.01 and minimum metric (confidence) = 0.90
2. Minimum support = 0.01 and minimum metric (confidence) = 0.95
3. Minimum support = 0.01 and minimum metric (confidence) = 0.99
4. Minimum support = 0.10 and minimum metric (confidence) = 0.90
Fraud Deterrence Model
Copyright by Pornchai Naruedomkul, Ph.D., CFE
7 attributes could not find the best rules at minimum
support of 0.10 and minimum metric (confidence) of 0.90.
Only one attribute could not find the best rules at
minimum support set up of 0.01 and minimum metric
(confidence) set up of 0.95 and 0.99.
All contributes could find the best rules at minimum
support set up of 0.01 and minimum metric (confidence)
set up of 0.90 and the rules would be used to develop
fraud deterrence model.
Fraud Deterrence Model
Copyright by Pornchai Naruedomkul, Ph.D., CFE
The meaning of the rules can be explained as follows:
Example: Rule No.1
X → Y
Addiction problems
and borrow money
from coworkers → Living beyond means
(Fraud risk behavior) (Fraud risk factor)
Probability to occur fraud = 0.92 (92%)
Best rules, min sup 0.1, min metric (confidence) 0.9
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Fraud survey was conducted with 280 companies from the
research of “Risk Factors for Fraudulent in the Organization
in Thailand” that indicated that they experienced the
corporate fraud in the past 3 years.
Frequency and consequences for those fraudulent activities
were examined based on risk management—Principles and
Guidelines (ISO 31000) of the International Organization
for Standardization.
Type and method of corporate fraud used in the
questionnaire were complied with the type and fraud
method of Association of Certified Fraud Examiners.
Fraud Risk Ranking Model
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Fraud Deterrence System Work Flow Process
Copyright by Pornchai Naruedomkul, Ph.D., CFE
To: Board of Directors of ABC Company Limited
Scope
We have performed the procedure agreed with you as detailed in the written instructions on March 1, 2011, and described below with respect to the fraudulent activities in the organization of ABC Company Limited as of December 31, 2011. The procedure was performed solely to assist you in evaluating potential employee frauds and is summarized as follows:
We obtained the data of attitude and behavior survey that was filled out by employees of ABC Company Limited as of December 31, 2011. We used the data to evaluate potential employee frauds.
Findings
We report as follows:
With respect to the above we found that the significant risk factors due to fraudulent activities in your organization are lack of authority, past legal, past employment, wheeler-dealer attitude, and inadequate pay. Furthermore, the analysis indicates that 16 out of 17 persons could be potential fraudsters.
For more details, please refer to the documents attached. Our report is solely for the purpose set forth in the first paragraph of this report and for your further action on risk management as well as risk minimization due to fraudulent activities. This report relates only to the items specified above.
Date Signature
Address
Report of Prediction Potential Fraud in the
Organization
Copyright by Pornchai Naruedomkul, Ph.D., CFE
ID Fraud Risk Factor Probability
1 - Mr. Charles Dolye Wheeler_Dealer 96.69 Past_employement
1 - Mr. Charles Dolye Past_legal 96.59 Close_relationship + Past_employement
1 - Mr. Charles Dolye Financial 93.00 Life_circumstances + Past_employement
1 - Mr. Charles Dolye Borrow_money 90.83 Close_relationship + Past_employement
1 - Mr. Charles Dolye Living 88.88 Close_relationship + Past_employement
2 - Mr. Sura Chan Wheeler_Dealer 94.74 Financial
2 - Mr. Sura Chan Living 88.88 Financial + Inadequate_pay
3 - Mr. Tony Gana Wheeler_Dealer 97.67 Financial + Past_employement
3 - Mr. Tony Gana Inadequate_pay 93.91 Family_problems + Past_employement
4 - Mr. Somsak Chanmeeboon Lack_authority 89.25 Life_circumstances + Pressure_organization
5 - Mrs. Soontaree Chaiudom Wheeler_Dealer 97.67 Living + Financial + Borrow_money
5 - Mrs. Soontaree Chaiudom Inadequate_pay 93.91 Family_problems + Wheeler_Dealer + Borrow_money
5 - Mrs. Soontaree Chaiudom Addiction_problems 91.00 Living + Financial + Family_problems + Wheeler_Dealer + Borrow_money
6 - Mr. Sakchai Lertmongkol Unwilling 90.50 Life_circumstances + Pressure_organization + Lack_authority
6 - Mr. Sakchai Lertmongkol Lack_authority 89.25 Life_circumstances + Pressure_organization
7 - Mrs. Wan Wanna Close_relationship 97.67 Family_problems + Inadequate_pay
7 - Mrs. Wan Wanna Wheeler_Dealer 94.74 Financial
7 - Mrs. Wan Wanna Living 88.88 Financial + Inadequate_pay
9 - Mr. Sakka Changsook Close_relationship 97.67 Wheeler_Dealer + Irriabilities
9 - Mr. Sakka Changsook Wheeler_Dealer 97.67 Financial + Refusal
9 - Mr. Sakka Changsook Past_legal 96.59 Financial + Refusal + Wheeler_Dealer
9 - Mr. Sakka Changsook Inadequate_pay 93.91 Wheeler_Dealer + Irriabilities
9 - Mr. Sakka Changsook Unwilling 90.50 Refusal + Wheeler_Dealer + Irriabilities
9 - Mr. Sakka Changsook Pressure_family 89.43 Gambling + Refusal + Wheeler_Dealer
10 - Mr. Nares Meesook Wheeler_Dealer 97.67 Past_legal + Past_employement
10 - Mr. Nares Meesook Past_legal 96.59 Close_relationship + Past_employement
10 - Mr. Nares Meesook Borrow_money 90.83 Close_relationship + Past_legal + Past_employement
10 - Mr. Nares Meesook Living 88.88 Close_relationship + Past_legal + Past_employement
11 - Mr. Tawin Chaisri Close_relationship 97.67 Family_problems + Pressure_family
11 - Mr. Tawin Chaisri Wheeler_Dealer 97.67 Financial + Addiction_problems
11 - Mr. Tawin Chaisri Past_legal 96.59 Financial + Close_relationship
11 - Mr. Tawin Chaisri Inadequate_pay 93.91 Family_problems + Pressure_family + Close_relationship
11 - Mr. Tawin Chaisri Living 93.76 Financial + Addiction_problems
11 - Mr. Tawin Chaisri Financial 93.00 Pressure_family + Gambling
11 - Mr. Tawin Chaisri Addiction_problems 91.00 Living + Financial + Family_problems + Pressure_family + Close_relationship
11 - Mr. Tawin Chaisri Refusal 82.00 Pressure_family + Close_relationship
12 - Mr. Srichan Wanpen Past_legal 96.59 Financial + Close_relationship
12 - Mr. Srichan Wanpen Wheeler_Dealer 94.74 Financial
12 - Mr. Srichan Wanpen Living 88.88 Financial + Close_relationship
13 - Ms. Netdao Sopa Close_relationship 97.67 Family_problems + Inadequate_pay
13 - Ms. Netdao Sopa Wheeler_Dealer 97.67 Living + Close_relationship
13 - Ms. Netdao Sopa Past_legal 96.59 Financial + Close_relationship
13 - Ms. Netdao Sopa Living 95.72 Financial + Life_circumstances
13 - Ms. Netdao Sopa Inadequate_pay 93.91 Family_problems + Close_relationship
13 - Ms. Netdao Sopa Addiction_problems 91.00 Living + Financial + Family_problems + Inadequate_pay + Close_relationship
14 - Mr. Sak Rodboon Wheeler_Dealer 97.67 Living + Close_relationship
14 - Mr. Sak Rodboon Past_legal 96.59 Financial + Close_relationship
14 - Mr. Sak Rodboon Living 88.88 Financial + Inadequate_pay
15 - Mr. Keng Chaikla Inadequate_pay 93.91 Close_relationship + Irriabilities
16 - Mr. Songsak Hanta Unwilling 90.50 Life_circumstances + Pressure_organization + Lack_authority
16 - Mr. Songsak Hanta Lack_authority 89.25 Life_circumstances + Pressure_organization
17 - Mr. Thai Rakdee Close_relationship 97.67 Life_circumstances + Irriabilities
17 - Mr. Thai Rakdee Inadequate_pay 93.91 Life_circumstances + Irriabilities
17 - Mr. Thai Rakdee Lack_authority 89.25 Life_circumstances + Pressure_organization
Fraud Risk Behaviors
Result from Fraud Deterrence Module
Testing—Potential Fraud Report
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Name Fraud Risk Factor Probability Fraud Risk Behaviors
8. Mr.Ake Kajonkul No Risk Factor 0 No Risk Behaviors
Result from Fraud Deterrence Module
Testing—Non-Potential Fraud Report
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Comparison of 1st and 2nd train test for fraud deterrence module
Name 1st Test 2st Test
1. Mr.Charles Dolye P P
2. Mr.Sura Chan P P
3. Mr.Tony Gana P P
4. Mr.Somsak Chanmeeboon X P
5. Mrs.Sonntaree Chaiudom P P
6. Mr.Sakchai Lertmongkol X P
7. Mrs.Wan Wanna P P
8. Mr.Ake Kajonkul P P
9. Mr.Sakka Changsook P P
10. Mr.Nares Meesook P P
11. Mr.Tawin Chaisri P P
12. Mr.Srichan Wanpen P P
13. Ms.Netdao Sopa P P
14. High Ranking Officer P P
15. Mr.Keng Chaikla P P
16. Mr.Songsak Hanta X P
17. Mr.Thai Rakdee P P
P Predict potential fraud correctly
X Predict poential fraud incorrecly
Fraud Deterrence System Verification
Copyright by Pornchai Naruedomkul, Ph.D., CFE
To: Board of Directors of ABC Company Limited
Scope
We have performed the procedure agreed with you as detailed in the written instructions on March 1, 2011, and
described below with respect to the fraudulent activities in the organization of ABC Company Limited as of
December 31, 2011. The procedure was performed solely to assist you in evaluating the level of risk due to
fraudulent activities in the firm and is summarized as follows:
We obtained the frequency, consequences, and details of risk issues due to fraudulent activities of ABC
Company Limited and used them to generate the level of risk for the organization.
Findings
We report as follows:
With respect to the above we found that asset misappropriation is the most popular for fraud committed:
72.72% (24 out of 33 cases) while corruption is 18.18% (6 out 33 cases) and fraudulent statement is 9.1% (3 out of
33 cases). The most important assets that the company should protect from fraudulent activities are cash on hand,
inventory, expense reimbursement, and fictitious of revenues and expenses.
For more details, please refer to the documents attached. Our report is solely for the purpose set forth in the
first paragraph of this report and for your further action on risk management as well as risk minimization due to
fraudulent activities. This report relates only to the items specified above.
Date Signature
Address
Report of Level of Risk Due to Fraudulent
Activities in the Organization
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Fraud risk ranking summary report
Company Name: A Accounting and Finance
Likelihood Low
Asset Misappropriation Impact Medium
Agree Risk Low
Likelihood
Corruption Impact
Agree Risk
Likelihood
Fraudulent Statements Impact
Agree Risk
Likelihood Low
Overall Impact Low
Agree Risk Low
Results from Fraud Risk Ranking Module Testing
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Company A
Department Name Risk No. Type of Fraud Fraud Method Fraud Risk Issue Likelihood Estimated % Loss Risk Level Likelihood Risk Level Impact Agreed Risk Status
Accounting and Finance 1 Asset Misappropriation cash-larceny-cashonhand Cashier steals money for personal use 6 3 High Medium High New
Accounting and Finance 2 Asset Misappropriationcash-fraudulent disbursements-
overstated expenseReimburse exceeding expenses without receipts 2 0.3 Very low Very low Very low New
Accounting and Finance 3 Asset Misappropriationcash-fraudulent disbursements-
multiple reimbursements
Use copy of the same receipt to reimburse the same
expense many times14 0.5 High Very low Low New
Accounting and Finance 4 Asset Misappropriation cash-skimming-unrecordedNot recording some sales transactions and receive
cash from customers7 1.5 Medium Very low Low New
Accounting and Finance 5 Asset Misappropriation cash-skimming-lappingschemes
Receive cash from clients and use it for personal use
and return the money to the company with the next
clients money
50 12 Medium Medium High New
Fraud Risk Ranking Report
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Example No. 1
Department Name = Accounting and Finance
Risk No. = 1
Type of Fraud = Asset Misappropriation
Fraud Method = Cash Larceny, Cash on hand
Fraud Risk Issue = Cashier steals money for personal use
Likelihood = 6
Estimated % Loss = 3
Risk Level Likelihood = High
Risk Level Impact = Medium
Agreed Risk = High
Status = New
Fraud Risk Ranking Report
Copyright by Pornchai Naruedomkul, Ph.D., CFE
A tool to indicate potential frauds in the organization.
Forensic accountant is needed for further investigation
for the staff who fits the potential fraud or is lower than
what it should be (i.e., reject all questions in the form).
Fraud Deterrence System
Copyright by Pornchai Naruedomkul, Ph.D., CFE
Thank you for your attention!