Innovation of Fraud Deterrence System in the Organization ... · learning, pattern recognition,...

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


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).


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.


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


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


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



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






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.



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


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


Fraud Deterrence System Work Flow Process


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


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


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


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


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


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


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


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


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


Thank you for your attention!

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