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Transactional Analysis for Effective Fraud Detection

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Title: Transactional Analysis for Effective Fraud Detection


1
Transactional Analysis for Effective Fraud
Detection
  • Doug Burton
  • ACL Services Ltd.

2
(No Transcript)
3
Todays Objectives
  • The magnitude of fraud
  • Fraud detection and internal controls
  • The role of technology
  • Continuous monitoring for fraud

4
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

5
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
6
What is Your Cost of Fraud?
  • 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
7
What is Your Cost of Fraud?
  • 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

8
Examples Occupational Fraud and Abuse
  • Embezzlement/asset misappropriations
  • Bribery
  • Bid-rigging
  • Conflict of interest
  • Fraudulent statements

85
2
9
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 monthsbefore
    detection
  • More stats www.cfenet.com/media/statistics.asp

Kate Head University of South Florida
10
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

11
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.

12
Commonly Detected Frauds
  • Accounts payable
  • Phantom vendors
  • Purchasing
  • Purchase splitting
  • Kickbacks
  • Purchase cards
  • Inappropriate, unauthorized purchases
  • Telecom
  • Inappropriate use of telephone system

13
Data Analysis in Fraud Detection
14
Data Analysis in Fraud Detection
  • 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

15
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

16
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
17
The Traditional Role of the Auditor in Detecting
Fraud
  • Based on examining samples of transactions

10,000 Employees
X 26 Pay Periods
260,000 paychecks/transactions
1 check .0004
10 checks .004
100 checks .04
1,000 checks .4
18
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
19
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
20
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

21
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)

22
The Role of Technology in Fraud Detection and
Investigation
  • 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

23
Examples of Fraud Tests
  • Questionable Purchases
  • P.O. with blank / zero amount
  • P.O. / invoices with amount paid gt amount
    received
  • Questionable purchases of consumer items

24
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

25
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

26
Examples of Fraud Tests
  • Phantom and other vendor tests
  • Vendor/employee name match
  • Employee and vendor with same address orphone
    number
  • Vendor address is a mail drop
  • High number of returns by vendor
  • Payment without invoice
  • Missing inventory
  • Duplicate documents

27
Assessing Risk
Measure Impact Based on Expected Occurrences
HIGH
HIGH
MODERATE
Probability of Occurrence
MODERATE
LOW
MODERATE
LOW
HIGH
Financial Impact
28
Challenges to Effective Fraud Detection
  • Data sampling
  • Disparate data sources complex IT systems
  • Ad hoc analysis

29
Issues With Sampling
  • Sampling is only effective with problemsthat 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

30
Examine Abnormalities
Random Sample
31
Establish Appropriate Parameters
32
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

33
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

34
Compare Information from Disparate Data Sources
Access data from two or more separate sources
35
Compare Information from Disparate Data Sources
Access data from two or more separate sources
Convert/harmonize data into comparable structures
36
Compare Information from Disparate Data Sources
Access data from two or more separate sources
Convert/Harmonize data into comparable structures
Combine data into single or related file for
analysis
37
Compare Information from Disparate Data Sources
Access data from two or more separate sources
Convert/Harmonize data into comparable structures
Combine data into single or related file for
analysis
Exceptions
38
Fraud Detection throughContinuous 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

39
Continuous Monitoring Process
  • Other Sources
  • Master Files
  • Related Data
  • Other References

Primary Transaction Data
Data Output
40
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

41
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

42
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

43
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

44
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

45
Thank you!
46
For More Information
  • Doug Burton
  • ACL Services Ltd.
  • Doug_burton_at_acl.com
  • 604-646-4201
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