My VisualA  |   Newsletter  |   Partners         Welcome
Technical Articles
Knowledge Base

Link Charts
V5I1206 - Financial Intelligence Units (FIUs)
V5I0806 - Money Laundering: The Exception
V5I0406 - Network Monitoring
V5I0106 - Filing Compliance
V4I0405 - Terrorism Financing
V4I0305 - Telephone Toll Analysis
V4I0205 - Wire Transfers for Alien Smuggling
V4I0105 - Bust-out Schemes
V3I1204 - Structuring Financial Transactions
V3I1104 - Finished Intelligence (Proactive Analysis)
V3I1004 - Exposing Mortgage Fraud
V3I0904 - MIND Lab Integrates Course Data
V3I0804 - Suspicious SAR-MSB Filing Data
V3I0704 - Integrating Multiple Data Sources
V3I0604 - Analyzing Airline Profitability
V3I0504 - Corporate Fraud
V3I0404 - Employee Master File Analysis
V3I0304 - Prescription Fraud Patterns
V3I0204 - Social Network Analysis (SNA)
V3I0104 - Fraud Detection System (FDS)
V2I1203 - Integration with our Digital Information Gateway
V2I1103 - Financial Transactions Investigation
V2I1003 - Compliance Analysis
V2I0903 - Medical Insurance Claims Analysis
V2I0803 - Corporate Fraud Investigation
V2I0703 - Possible Domestic Terrorist Shooting
V2I0603 - Suspicious Activity Report (SAR) Filing
V2I0503 - Detecting Financial Crimes
V2I0403 - "Referential" Data Sources
V2I0303 - Proactive Analyses
V2I0203 - Transactional Activities
V2I0103 - Temporal Grid

White Papers

Frequently Asked Questions


Referenced in our Newsletter Volume 3, Issue 5 - May 2004

Corporate Fraud

This month's Link Chart continues last month's discussion on exposing different types of situations that potentially indicate corporate fraud.

One of the more power features of VisuaLinks is its ability to provide high-level counts and abstractions of the underlying data using the Summarize service. Similar to traditional OLAP (On-Line Analytical Processing), Summarize is one of the quickest ways within VisuaLinks to get a breakdown of various data fields such as payments, amounts, dates, and other content where multiple (repeating) instances of the values is considered questionable behavior. Thus, Summarize is often used for understanding transactional behaviors such as payment and invoicing frauds. (Hint: Summarize should be used when a question starts with "How many…")

In the following diagram, the results of a Summarize request show the top payments, in terms of frequency, in the system based on the vendor master file.

The *COUNT column represents the total number of payments followed by the *GROUPED BY AMOUNT and *GROUPED BY NAME columns. Each row in the Summarize results presents the total number of payments made to a specific vendor for a specific dollar amount. The *GROUPED BY NAME column containing the payee information is intentionally hidden by a gray box to conceal the names.

The blue arrows depict the same vendor, which in this case, represents the name of an individual. What is immediately revealed is that this person has been paid 64 times for the exact amount of $96.15 (shown by the top arrow). The nature of her business is unclear; however, basic breakdowns for monthly or weekly reimbursements do not correlate to this type of known payment frequency (e.g., monthly parking, internet fees, mileage reimbursement, lease or rental costs, etc.).

Additionally, the second arrow shows 18 payments of $192.30 - which is exactly double the $96.15 payment amount ($96.15 x 2 = $192.30). This can be considered an additional 36 payments of $96.30 which conceptually brings our total up to exactly 100 payments of $96.30. Furthermore, there are no other payment amounts for this vendor in the data, only the amounts previously described. Summarize supports the drill-down of any of the data returned and subsequent review was conducted.

The next diagram takes the results shown previously with the *GROUPED BY AMOUNT column resorted to show the highest payment amounts.


The blue arrows show a particular payee (a corporation) receiving some of the largest payment amounts recorded. The concern here is to determine if the payments are part of a financing plan (e.g., equipment, construction) or if the payments potentially represent duplicate payments for the same invoices. The red arrows show the payments for a different company where the amounts in question tend to be more "rounded" (whole numbers - no cents) and "clustered" around the same range. In this case, there are 12 payments clustered around the $30,000 range. Additional review of both payment scenarios was initiated to determine the nature of these payments.

The final diagram shows the results of a Summarize request where the total amount for all the invoices were summed up for each vendor.


The results on the left are sorted based on the count of the number of invoices processed and the results on the right are sorted by the total value of all invoices paid. This diagram has been annotated with lines and arrows to show where the same vendor appears in both results. One interesting observation is that only 6 of the most frequently paid vendors (left) also account for the most cost (right) to the company. Additional functions could have been applied to these results to also show the MIN and MAX values for any particular payment.



If you found this link chart helpful you can subscribe to our newsletter to receive a new link chart each month in your email.