Transactional Analysis for Effective Fraud Detection Doug Burton ACL Services Ltd.

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    24-Dec-2015

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  • Slide 1
  • Transactional Analysis for Effective Fraud Detection Doug Burton ACL Services Ltd.
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  • ELMER IREY Chief of the US Treasury Enforcement Branch and head of the IRS Special Intelligence Unit (formed in 1919 primarily to combat employee crime) Instrumental in convicting Al Capone of tax evasion Served as an ally and partner to law enforcement Americas first high profile Forensic Accountant
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  • Todays Objectives The magnitude of fraud Fraud detection and internal controls The role of technology Continuous monitoring for fraud
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  • Occupational Fraud and Abuse The use of ones occupation for personal enrichment through the deliberate misuse or misapplication of the employing organizations resources or assets Deception brought about by the willful misrepresentation of significant material facts, or silence when good faith requires expression, resulting in material damage to one who relies on those facts and has a reasonable right to do so An intentional act which is concealed, resulting in a personal benefit to the perpetrator and resulting in harm to the organization
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  • What is Your Cost of Fraud? U.S. organizations lose about $4,500 per employee annually as a result of occupational fraud and abuse* How many employees do you have? * Association of Certified Fraud Examiners, 2002 Report to the Nation on Occupational Fraud and Abuse 5,000 Employees X $ 4,500 $ 2,250,000 Annual Cost of Fraud
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  • U.S. organizations, on average, lose 6% of revenues to fraud. This represents a potential loss of $600 billion to fraud annually within the U.S. What is your annual gross revenue ? * Association of Certified Fraud Examiners, 2002 Report to the Nation on Occupational Fraud and Abuse $ 10,000,000 Annual Revenue X.06 $ 600,000 Annual Cost of Fraud What is Your Cost of Fraud?
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  • In addition to the direct cost of fraud, there are significant indirect costs: Loss of consumer confidence = reduced revenues Negative PR image = lower stock values Low employee morale = lower productivity Inability to retain and attract qualified staff What is Your Cost of Fraud?
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  • Examples: Occupational Fraud and Abuse Embezzlement/asset misappropriations Bribery Bid-rigging Conflict of interest Fraudulent statements 85% 13% 2%
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  • Other Statistics Most commonly detected through tips Next most common is by accident Only 7% of fraudsters had prior fraud-related convictions Know your F.A.C.T.S.* (Fraud is Always Committed by Trusted Souls) Average fraud scheme lasts 18 months before detection More stats: www.cfenet.com/media/statistics.asp * Kate Head University of South Florida
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  • Fraud Detection and Internal Controls These (improper) payments occur for many reasons including insufficient oversight or monitoring, inadequate eligibility controls, and automated system deficiencies. However, one point is clear the basic or root cause of improper payments can typically be traced to a lack of or breakdown in internal controls. GAO report on Coordinated Approach Needed to Address the Governments Improper Payments Problems [August 2002]
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  • Sarbanes-Oxley Requirements Section 302 - Management certification to integrity of Internal Controls must address 4 key points: Statement of managements responsibility for establishing and maintaining adequate internal controls Managements assessment of the effectiveness of internal controls to include all fraud involving management and employees with significant roles in internal control A statement identifying the framework used by management as a criteria for evaluating control effectiveness A statement that the independent accountant has also issued an attested to managements assessment of internal control.
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  • Commonly Detected Frauds Accounts payable Phantom vendors Purchasing Purchase splitting Kickbacks Purchase cards Inappropriate, unauthorized purchases Telecom Inappropriate use of telephone system
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  • Data Analysis in Fraud Detection
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  • Los Angeles Unified School District - Belmont Learning Center ACL use resulted in the identification of fraud and abuse in excess of $70 million Fictitious vendors Duplicate payments Over-billing No competitive bidding Policy violations Exceeding purchasing limits Improper coding Data Analysis in Fraud Detection
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  • The Traditional Role of the Auditor in Detecting Fraud Typically a reactive role tips Based on examining selected samples of transactions Testing of existing controls ACFE survey says 90% of managers place their confidence in internal controls Limited use of technology
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  • The Traditional Role of the Auditor in Detecting Fraud Typically a reactive role The longer frauds go undetected, the larger the potential for loss and the smaller the chances of recovery
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  • 10,000 Employees X 26 Pay Periods 260,000 paychecks/transactions 1 check.0004 % 10 checks.004 % 100 checks.04 % 1,000 checks.4 % The Traditional Role of the Auditor in Detecting Fraud Based on examining samples of transactions
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  • The Traditional Role of the Auditor in Detecting Fraud Testing of existing controls 46% of frauds occurred because of insufficient controls An additional 40% of frauds exploited situations where controls were ignored
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  • The Traditional Role of the Auditor in Detecting Fraud Limited use of technology Both the AICPA and the ACFE specifically refer to the use of data analysis to assist in fraud detection
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  • The Role of Technology in Fraud Detection and Investigation Perform risk analysis Look for indicators of fraud Review 100% of transactions Compare data within different databases and computer systems Determine impact of fraud Proactive tests Continuous monitoring
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  • Discovering Fraud Electronically Three Approaches Drill-down Analysis Review large population and determine true areas of risk Isolate red flags and drill down Attribute Sampling Begin with entire population and filter for transaction matching specific criteria File Matching Compare separate data files and look for disparities or matches (e.g. phantom vendors)
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  • Data analysis will provide: Indication of where to look Indication of the depth and scope of the problem Direct pointers to critical evidence Proof Findings The Role of Technology in Fraud Detection and Investigation
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  • Examples of Fraud Tests Questionable Purchases P.O. with blank / zero amount P.O. / invoices with amount paid > amount received Questionable purchases of consumer items
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  • Examples of Fraud Tests Questionable Invoices Invoices without a valid P.O. Invoices from vendors not in vendor file Invoices for more than P.O. authorization Multiple invoices for same item description Vendors with duplicate invoice numbers High/inconsistent prices
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  • Examples of Fraud Tests Questionable Invoices Invoices for same amount on the same date Multiple invoices for same P.O. and date Sequential invoices Invoices with no matching receiving report New or non-approved vendors
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  • Examples of Fraud Tests Phantom and other vendor tests Vendor/employee name match Employee and vendor with same address or phone number Vendor address is a mail drop High number of returns by vendor Payment without invoice Missing inventory Duplicate documents
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  • Moderate to High Risk Moderate Risk High Risk Moderate Risk Moderate to High Risk Low Risk Moderate Risk Low Risk Assessing Risk Measure $ Impact Based on Expected Occurrences Probability of Occurrence Financial Impact LOWHIGH Low Risk LOW HIGH MODERATE
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  • Challenges to Effective Fraud Detection Data sampling Disparate data sources; complex IT systems Ad hoc analysis
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  • Issues With Sampling Sampling is only effective with problems that are relatively consistent throughout a data population Fraudulent transactions by nature do not occur randomly Fraudulent transactions often fall within bounds for standard testing and therefore do not get flagged
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  • Examine Abnormalities Random Sample
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  • Establish Appropriate Parameters Acceptable Range
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  • Benfords Law Testing What is it? Benfords Law tells us that numbers occur with predictable frequency within a natural population The digits 1 9 appear with declining frequency: 1 = 30% 9 = 4.6% This natural rule, applied to a numeric population, can point to numbers appearing more frequently than normal, thus being suspect
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  • Benfords Law - Example Audit review of physician billings Benfords Law testing identified a spike in the number 3 Of these records, 22 percent were submitted by one doctor Subsequent analysis revealed impossibly high daily billings
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  • Access data from two or more separate sources Compare Information from Disparate Data Sources
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  • Convert/harmonize data into comparable structures Access data from two or more separate sources Compare Information from Disparate Data Sources
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  • Convert/Harmonize data into comparable structures Combine data into single or related file for analysis Access data from two or more separate sources Compare Information from Disparate Data Sources
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  • Exceptions Convert/Harmonize data into comparable structures Combine data into single or related file for analysis Access data from two or more separate sources Compare Information from Disparate Data Sources
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  • Fraud Detection through Continuous Monitoring Data analysis is used in fraud detection & investigation to identify & document fraudulent activities Part of overall fraud detection plan Investigate and document issues identified Continuous monitoring analyzes three key areas: Identifies anomalies within data files/transactions Examines 100% of the data (not sampling) Timely identification (not suspicious transactions) Runs automatically (user-defined frequency); reports anomalies to designated individuals for investigation
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  • Reporting Medium Continuous Monitoring Process Primary Transaction Data Other Sources: Master Files Related Data Other References Data Output FRAUD TESTS DATA ANALYSIS
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  • Data Analysis in Fraud Detection A US government agency with $6.5 billion in annual procurement card purchases used data analysis to monitor expenditures Indicators of inappropriate transactions were established and compared to actual data Data from disparate sources were integrated including employee listings, authorizations, merchant restrictions, credit limits $38 Million in suspect transactions were identified A timely and cost-effective reporting system was created to follow-up with vendors and banks in the subsequent recovery process
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  • Data Analysis in Fraud Detection A large healthcare insurer was defrauded of more than $25 million in claims A routine claims audit identified an abnormal number of transactions of a certain value (through data analysis) By implementing a continuous monitoring application, the organization may have identified the anomalies earlier in the process Fraud exposure would have been reduced Process improvements would have been identified
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  • Benefits of Continuous Monitoring Confirms/validates effectiveness of controls Mitigates deficient control structures Monitors data from disparate systems to provide holistic view of transactions Provides independent assurance Identifies further process improvement opportunities Identifies suspicious transactions in a timely manner Reduces waste, enhances recoveries
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  • Status of Continuous Monitoring Fastest growing area within audit and control community Increasingly more common in organizations Organizational challenges for widespread implementation: Technological barriers; difficulties of access to data Assumption that effective application controls are in place Perception that sampling is an effective control assessment methodology Lack of detailed understanding of exactly what and how to test Recommendation seek expert advice
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  • Implementation of a Fraud Detection Program 1.Build a profile of potential frauds which can then be tested 2.Analyze data to identify possible indicators of fraud 3.Implement continuous monitoring of high-risk business functions to automate the detection process 4.Investigate and drill down into patterns which emerge via data analysis/detection process
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  • Thank you!
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  • For More Information Doug Burton ACL Services Ltd. Doug_burton@acl.com 604-646-4201