CFE Exam Quiz 31

There are significant advantages to using data analysis software. These advantages include:

– Data analysis software allows the fraud examiner to centralize an investigation, relying less on others to gather data.
– Data analysis software allows the fraud examiner to ensure that an investigation is accurate and complete.
– Data analysis allows the fraud examiner to base predictions about the probability of a fraudulent situation on reliable statistical information.
– Data analysis allows the fraud examiner to search entire data files for red flags of possible fraud.
– Data analysis assists the fraud examiner in developing reference files for ongoing fraud detection and investigation work.

Computers can scan database information for several specific types of information, creating a red-flag system. To perform this, most software packages use a combination of different functions. These functions include:

– Sorting
– Record selection
– Joining files
– Multi-file processing
– Correlation analysis
– Verifying multiples of a number
– Compliance verification
– Duplicate searches
– Expressions and equations
– Filter and display criteria
– Fuzzy logic matching
– Gap tests
– Pivot tables
– Regression analysis
– Sort and index
– Statistical analysis
– Stratification
– Date functions
– Benford’s Law analysis
– Graphing

Sorting is the arrangement of data in a meaningful order. Sorted data aids fraud examiners to quickly pick out what is important or identify blank fields where information should be entered.

A pivot table is an interactive data summarization tool found in Excel. It is used to sort, count, total, or give the average of specified data in a spreadsheet. Suppose you have a spreadsheet with sales data, a pivot table will allow you to determine how many sales occurred by month, by country, by product, by salesperson, and so on.

Gap tests are used to identify missing items in a sequence or series, such as missing checks or invoice numbers. It can also be used to find sequences where none are expected to exist (e.g., employee government identification numbers).

Regression analysis is a sophisticated statistical technique that uses a series of records to create a model relationship between a dependent variable and one or more independent variables. An example of the use of regression analysis is in the modeling and predicting of the number of widgets manufactured based on amounts of materials and labor used.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of the general ledger:

– Select specific journal entries for analysis.
– Create actual-to-budget comparison reports.
– Analyze and confirm specific ledger accounts for legitimate transaction activity.
– Speed account reconciliation through specialized account queries.
– Calculate financial ratios.
– Calculate percentage comparison ratios between accounts.
– Prepare custom reports, cash flow, profit/loss, and asset and liability total reports.
– Compare summaries by major account in any order (low-high, high-low).
– Create reports in any format by account, division, department, etc.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of accounts receivable records:

– Create a list of customer limit increases and decreases.
– Age accounts receivable in various formats.
– Identify gaps in sequential forms such as invoices.
– Identify duplicate invoices or customer account numbers.
– Show specified reports on credits taken by customers.
– Report customer summaries by invoice, product, etc.
– Identify customer activity by age, product, etc.
– Compare customer credit limits and current or past balances.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of cash disbursement records:

– Summarize cash disbursements by account, bank, department, vendor, etc.
– Verify audit trail for all disbursements by purchase order, vendor, department, etc.
– Generate vendor cash activity summary for analysis.
– Identify disbursements by department, supervisor approval, or amount limits.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of accounts receivable records:

– Track scheduled receipt dates versus actual receipt dates, in summary, and in detail.
– Compare vendor performance by summarizing item delivery times and amounts.
– Isolate purchase order types for analysis.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of payroll records:

– Summarize payroll activity by specific criteria for review.
– Identify changes to payroll or employee files.
– Compare timecard and payroll rates for possible discrepancies.
– Prepare check amount reports for amounts over a certain limit.
– Check proper supervisory authorization on payroll disbursements.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of asset accounts:

– Generate depreciation to cost reports.
– Compare book and tax depreciation and indicate variances.
– Sort asset values by asset type or monetary amount.
– Select samples for asset existence verification.
– Recalculate expense and reserve/provision amounts using replacement costs.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of sales information:

– Create a report of all system overrides and sales exceptions.
– Analyze returns and allowances by store, department, or other areas.
– Summarize trends by customer type, products, salesperson, etc.
– Compare ratios of current sales to outstanding receivables or other variables.
– Generate reports on a correlation between product demand or supply and sales prices.

The following are examples of data analysis queries that can be performed to detect fraud through the examination of accounts payable:

– Audit paid invoices for manual comparison with actual invoices.
– Summarize large invoices by amount, vendor, etc.
– Identify debits to expense accounts outside of set default accounts.
– Reconcile check registers to disbursements by vendor invoice.
– Verify vendor tax forms (e.g., U.S. Form 1099 or Value Added Tax (VAT) forms).
– Create vendor detail and summary analysis reports.
– Review recurring monthly expenses and compare them to posted/paid invoices.
– Generate a report on specified vouchers for manual audit or investigation.

The data analysis checklist includes some key issues to address as one conducts data analysis. The following are some key issues to address as one conducts data analysis:

– Ensure data validity and data integrity.
– Consider the data format and structure.
– Compare the cost and benefit of potential analysis.
– Consider the spectrum of distinct levels of aggregation at which fraud monitoring is required.
– Begin with the end in mind.

A ghost employee is someone who is on the payroll register but who does not actually work for the company. A ghost employee scheme is one common asset misappropriation scheme. Finding ghost employees often coincides with an internal fraud scheme.

If a fraud examiner suspects a ghost employee scheme after gathering data, he would first need to determine what kind of tests to run. The following are some tests that can help to uncover ghost employee schemes:

– Multiple employees using the same bank account for direct deposit.
– Invalid or blank tax/government identification numbers.
– Multiple employees with the same tax/government identification number.
– Employees on the payroll register prior to their start date or after their termination date.
– Non-salaried employees on the payroll register but not in the timekeeping system.
– Employees on the payroll register but not in the employee master file.
– Employees with no deductions for taxes or benefits (where applicable).
– Employees with no pay increases, or more than two pay increases, in the past year.

Structured data is the type of data found in a database, consisting of recognizable and predictable structures. Data analysis in the traditional sense involves running rule-based queries on structured data.

Unstructured data is data that would not be found in a traditional spreadsheet or database. This is in contrast to structured data. Unstructured data is typically text-based.

Structured data is the type of data found in a database, consisting of recognizable and predictable structures. Examples of structured data include sales records, payment or expense details, and financial reports.

Unstructured data is the type of data that would not be found in a traditional spreadsheet or database. Unstructured data comes from numerous sources. Sources of unstructured data include social media posts, instant messages, videos, voice files, user documents, mobile phone software applications, news feeds, and sales and marketing materials.

Textual analytics is a method of using software to extract usable information from unstructured text data. Textual analytics software can categorize data to reveal patterns, sentiments, and relationships indicative of fraud. Textual analytics provides the ability to uncover additional warning signs of potentially fraudulent employee behavior.

The short-form of fraud examination reports summarizes the issues presented, the steps taken, and the information found during the course of the fraud examination.

The long-form of fraud examination reports contains the substantive work produced during the fraud examination, including copies of relevant documents and memoranda of witness interviews.

As a general rule, financial information is private and can be obtained only by legal process or consent.

Depending on the type of fraud risk present in an investigation, a fraud examiner will want to come up with a list of fraud keywords that are likely to point to suspicious activity. Keywords that might indicate pressure include deadline, quota, trouble, short, problem, and concern.

Depending on the type of fraud risk present in an investigation, a fraud examiner will want to come up with a list of fraud keywords that are likely to point to suspicious activity. Keywords that might indicate that someone has the opportunity or ability to commit fraud include override, write off, recognize revenue, adjust, discount, and reserve/provision.

Depending on the type of fraud risk present in an investigation, a fraud examiner will want to come up with a list of fraud keywords that are likely to point to suspicious activity. Keywords that might indicate a fraudster is rationalizing his actions include reasonable, deserve, and temporary.

The relationship between banks and their customers is confidential, therefore information from banks can be obtained only by a subpoena or other legal processes. Requested records and the information they provide could include the master file of customers, the safe deposit box records and the account opening records.

The account opening records for a personal account might contain handwriting samples and personal information about account holders, varying from one bank to another. Typically, the account opening records for a personal account contain the account holder’s name, address, telephone number, tax identification number, amount of the account’s opening deposit, and security data.