Principles of Fraud Examination - ACFE

© 2018 Association of Certified Fraud Examiners, Inc.

Principles of Fraud

Examination

Analyzing and Managing Financial Information

© 2018 Association of Certified Fraud Examiners, Inc. 2 of 27 CPE PIN: POFE816

Practical Problem

Questions:

1. Name one or two of the existing software

packages that you think are best for analyzing

data to detect fraud.


Practical Problem

Questions:

2. What minimum analysis would you

recommend for the following fraud schemes?

a. Accounts payable; fictitious vendors

b. Journal entries

c. Fictitious revenues


Analyzing and Managing Financial Data

▪ The data tells the story:

• Timelines

• Relationships

• Patterns

• Amount of losses

• Omissions and manipulations


Data Analysis Applications—General

▪ Accuracy: check totals and calculations

▪ Analytical review: comparisons, profiling, etc.

▪ Validity: duplicates, exception

▪ Completeness: gaps, matches, totals

▪ Cut-off: date and number sequences analysis

▪ Statistical sampling: selection

▪ Summarization and ranking: customers, products, regions

▪ Performance measures: response times for order processing


Data Analysis Applications—Fraud

▪ Questionable invoices:

• Multiple invoices with same amounts or

descriptions

• Duplicate invoices or invoices in sequence

▪ Duplicate payments

▪ Vendor schemes:

• Vendors matching employees

• Vendors with one or more address or vendor code

• New vendors with high activity


Data Analysis Applications—Fraud

▪ Kickbacks and conflicts of interest:

• Vendor prices higher than standard

• Check for continued purchases despite high rates

of returns, rejects, credits, etc.

▪ Financial statement fraud:

• Ratio, vertical, and horizontal analysis


Understanding Accounting Software

▪ Tables:

• Customers

• Vendors

• Employees

• Inventory

▪ Transaction files:

• Disbursements

• Sales detail

• General ledger

▪ Reports

▪ Audit trail


Identifying Necessary Documents and Data

▪ Internal data:

• Email

• MS Office files (Excel, Word, etc.):

• Server

• PC

• Network or software log-in data

• Other pertinent documents:

• Contracts

• Deposit slip books

• Invoices (both A/P and A/R); shipping documents, etc.


Identifying Necessary Documents and Data

▪ External data:

• Bank statements and canceled checks

• External travel and entertainment histories

• Payroll service reports

• Public document searches:

• Secretary of state

• County treasurer

• Background checks

• Social media searches


Data Analytics Software

▪ Data analysis software

can take the total

population of data,

analyze it, and identify

anomalies.

▪ Fraud examiners do

not have to be

programmers to use

the software.


Data Analytics Software

▪ Common applications

used to analyze data:

• ACL

• IDEA for Windows

• Spreadsheets


Data Extraction Realities

▪ Time consuming

▪ Difficult to retrieve “clean” data

▪ Difficult to retrieve accurate data

▪ Cooperation of client and client’s IT

personnel


Preparing Data for Analysis

▪ Preparing the data:

• Standardized

• Accurate

▪ Common errors and issues:

• Recurring headers or footers

• Text versus numbers

• Date formats

▪ Keep initial file unaltered

▪ Track changes to initial file


Examples of Data Analysis

Techniques—Purchasing

Function Detection

Dates of bids• Unauthorized information release—same vendor

always bids last

Bidding patterns • Favorable treatment to select vendors

Favored-vendor

status

• Fictitious vendors, duplicate payments, or favorable

treatment

New vendor hiring

patterns• Fictitious vendors or conflicts of interest

Vendor addresses • Fictitious vendors or conflicts of interest

Override transactions • Fictitious vendors or duplicate payments

Overbilling • Unusual, or one-time extra charges

Conflict of interest

• Vendors with employees who are related to the client

• An unusually high occurrence rate of complaints

• Compliments about specific vendors

• Higher prices and/or substandard quality



Techniques—Accounts Receivable

Function Detection

Age and filter receivables • Identify postings to dormant accounts.

Export data• Prepare confirmation letters to

customers.

Sequence data

• Review the logical range (pre-numbered,

chronology, etc.) of credits, invoices, and

payments.

Age data• Review credits beyond discount terms

(amounts or dates).



Techniques—Inventory

Function Detection

Summarize and extract

data

• Compare physical count with computed

count.

Classify or subtotal • Calculate returns and shortages by vendor.

Filter, summarize, and

sample data

• Identify high-value items purchased by

internal buyers.

Extract data • Review pricing on comparable products.

Filter and classify data• Determine inventory sent to scrap by

employee.


Examples of Data Analysis Techniques—

Disbursements and Accounts Payable

Function Detection

Filter• Summarize large invoices without

purchase orders by vendor.

Join• Identify vendor unit price variances by

product over time.

Extract data • Review pricing on comparable products.

Join• Reconcile check register to

disbursements by vendor invoice.

Join• Compare monthly expenses to

posted/paid expenses.



Techniques—Payroll

Function Detection

Sample• Review overtime, pay, bonuses,

commissions.

Duplicates

• Identify duplicate direct deposit numbers.

• Identify duplicate employee names, phone

numbers, etc.

Age• Compare employee start and termination

date with pay dates.

Filter • Identify payroll with no deductions.


Benford’s Law

▪ First digits in a sequence of numbers do not

fall equally

▪ Applies to natural occurring numbers

▪ Well suited to detect anomalies

▪ Applications:

• Payments

• Authorization levels

• Excessive rounding:

• Analyze the last two digits of the numbers.


Benford’s Law


Using Financial Analysis to

Detect Illicit Income

▪ Two basic methods:

• Net-worth or asset

method

• Expenditures method


Net Worth—Asset Method

Year 1 Year 2

Assets $234,320 $280,150

– Liabilities 132,000 115,800

= Net Worth $102,320 $164,350

– Prior Year’s Net Worth 5,905 102,320

= Increase in Net Worth $96,415 $62,030

+ Known Expenses 16,250 29,450

= Total Net-Worth Increase $112,665 $91,480

– Funds from Known Sources 39,685 41,455

= Funds from Unknown Sources $72,980 $50,025


Rules of Net-Worth Analysis

▪ All assets should be valued at cost.

▪ Estimate the amount of funds available

generously.

▪ Conservatively estimate the amount of living

expenses.


Expenditures Method

Year 1 Year 2

Known Expenditures $117,865 $91,480

– Known Source of Funds 44,885 41,455

= Funds from Unknown Sources $72,980 $50,025


Software Used in Organizing

and Presenting Cases

▪ i2-Analyst Notebook:

• Link analysis

• Network analysis

• Timeline analysis

• Transaction pattern

• Database visualization

▪ CaseMap

▪ Spreadsheets and database software