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To Catch A Thief |
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There are all sorts of variations on this theme, especially when dealing with times and dates. Since transactional patterns are structurally consistent, the same type(s) of patterns can be derived from telephones, border crossings, travel events, email, web-site visits (cyber-crime), or just about anything else with a temporal value.
Ideally when dealing with financial crimes, the ultimate goal is to seize the assets associated with the money laundering operations. Thus, if a temporal pattern can be identified and confirmed, then the law enforcement agency has a better chance of actually obtaining a "cash" assets forfeiture because they can predict when the funds are going to be moved or when the accounts are full.
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Conclusions
There are good people - and there are bad people. The bad people cost the good people a significant amount of monetary and resource losses (measured in billions of dollars) through the liabilities incurred from fraud, theft, espionage, embezzlement, public corruption, and proliferation.
In many cases the malpractice and malfeasance succeed because people do not know how to interpret their data sets or recognize the telltale symptoms. The majority of wrongdoing is carried out in a large number of relatively small exchanges. A large percentage of crimes such as money laundering are perpetrated through a series of frequent transactions with relatively small amounts of money being processed on any one occasion. This sort of activity is of course subtle and not directly detectable through usual methods of oversight. To catch a thief, or any wrongdoer for that matter, one must lock onto a behavior pattern. Data mining and visualization approaches can be applied to these sorts of problems with great success at relatively low cost.
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