Fraud analytics starts with a theory. Theory has assumptions and some gut factor. Think about it, all our life from childhood to adult life we are involved in search-n-seek. Childhood days it's cookies, candies and so on, but as we grow up car keys, glasses, remote so on so forth; however the point to note is that the strategy to find things constantly evolves. Perhaps, to find fraud in business process and systems, the search-n-find skill need to be taken to the next level.
The detection strategy should be such that is proactive and constantly audited. But, based on experience one need to outline some assumptions to identify trees in the forest. The approach to find the tree in the forest should be such that it can be modeled and be repetitive processes. These models and processes can be deployed by IT teams for business to monitor and evolve the pattern. Sounds simple, but a properly designed fraud plan begins with simply looking for instances where a fraud scenario is most likely to occur, much like a search-and-find game.
Effective fraud plan also requires awareness, or the ability to interpret the data for the indicators, of the fraud scenario. While the simple fraud scenarios can be detected via a properly designed fraud data procedure, a fraud scenario with a sophisticated concealment strategy requires the ability to see through the concealment strategy.
1. List your assumptions based on high probable cases. The key considerations are to
understand the variations of the scenario that are caused by the fraud opportunity. This helps define the scope of the Fraud Audit.
2. Develop a fraud data profile with data, using the process of drawing a picture of a fraud scenario. For example, one variation of a false billing scheme through a false company is when the accounts payable takes over the identity of a dormant vendor on the database and charges invoices to a large cost center.
3. Structured step-by-step approach to identifying transactions consistent with a fraud scenario/assumption, as described through the fraud data profile.
4. Obtain pertinent data and their relation to the assumption.
5. Define data interrogation procedure - pattern & Frequency, identify outlier cases for good and bad both, Trends, GAP in business process, potential mistakes in data capture and transactional history, Master data accuracy
6. Define the KPI (Key performance Indicator) and monitor the indicators regularly
7. Prepare plan to respond to the indicator pattern. Evolve the KPIs for further sophistication and insight.
8. Once the culprits is identified monitor their behavior for firming up the plan. This will also help evolve the good vs bad outliers.
Search routines help focus identifying of “red flags” of the fraud scenario/assumption. By using data interpretation, one can develop reports or documentation and interpret the data.
Insurance: In United States, about $67 billion is lost every year to fraudulent claim.(Federal Bureau of Investigation [FBI], 2003).
Telecommunications: $1.5 trillion phone industry loses approximately 10% to fraud, that is $150 billion at current estimates (Mena, 2003).
Bank Fraud: For the period of April 1, 1996 through September 30, 2002, the FBI received 207,051 Suspicious Activity Reports equaled approximately $7 billion in losses (U.S. Department of Justice [DOJ], 2002).
Money Laundering: United States Treasury officials estimate that as much as $300 billion is laundered annually, worldwide, with from $40 billion to $80 billion of this originating from drug profits made in the United States. (Mena, 2003).
Internet: According to Meridien Research, worldwide credit card fraud[the Internet component] will represent $15.5 billion in losses [annually] by 2005. However, if merchants adopt data mining technology now to help screen credit-card orders prior to processing, the widespread use of this technology is predicted to cut overall losses by two thirds to $5.7 billion in 2005” (Mena, 2003).
Credit Card: The numbers from the Nilson report indicate that issuer credit card fraud losses run approximately 1 billion dollars annually. This list does not even include debit card fraud, brokerage fraud, fraud at casinos, health care fraud, and other miscellaneous fraud types such as bankruptcy fraud
Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
Senator Everett Dirksen so aptly said, “A billion here a trillion there; the first thing you know, you’re talking about real money.”
Source: Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
1. Fraud By the Book
2. Medicare Fraud