In this paper, we have discussed different financial crimes that are seen today i.e credit card fraud,
card not present fraud, Loan default, Bank Fraud, Money Laundering, Insurance crime etc. Then we have
discussed how data mining becomes helpful for detection of these types of financial crimes. Today Industry is facing huge losses due to these types of financial crimes, so it would be able to find financial crime through data mining techniques and remove it then it can be great benefit to the industry. In this paper we have suggested a two-tier architecture model for financial crime detection. In the first stage the financial transaction is verified against the rule-based system and is given risk score by the system. These rules contain the human insight. And then this transaction is passed to second stage of data mining technique, which will learn from the past experience of fraudulent transactions and then decide about the current transaction.
[...] As with other financial crimes, detection must occur before any loss is sustained. There are lead indicators like the "manipulation of credit" described above and in the lack of references, high associations of matching attributes, and dubious acceptance criteria. The critical factors for detecting all of these financial fraud crimes is knowing the behavior of credit, bank, and loan accounts and developing an understanding of the categories of customers. Data mining can be used to spot outliers or account usages that are normal and out of character. [...]
[...] Link analysis may be used to look for a ring of fraudulent providers, and, of course, data mining tools, such as neural networks, may be used for training and detection if samples of fraud cases exist. The net amount of the claim may be too MISCODING In processing claims, insurance companies rely mainly on diagnostic and procedural codes recorded on the claim forms. Their computers are programmed to detect services that are not covered. Most insurance policies exclude nonstandard or experimental methods. [...]
[...] Here we suggested a two-stage solution for financial crime detection, which is actually hybrid approach and contains both human insight and machine insight also. In these type of crimes hybrid approach proves more powerful than any single stage solution and also accuracy of prediction is increased drastically. In this type of model or system, we also need to take care of that any normal or genuine transaction must not be caught by as fraudulent transaction and create overhead on customer. [...]
[...] The main method of detection is to look for outliers and changes in the normal patterns of usage. A SOM neural network can be used to perform an autonomous clustering of patterns in the data CARD-NOT-PRESENT FRAUD (ONLINE CREDIT CARD FRAUD) Internet and phone-order transactions are generally card-not-present (CNP) sales. They are also time-sensitive crimes, where the thieves are racing to beat the credit-card monthly statement mailing date. Internet credit-card thieves do leave characteristic footprints. For example, many businesses see fraud rates increase at certain times of the day, and orders coming in from certain countries exhibit a higher percentage of fraud. [...]
[...] In addition, a carrier may use models and rules developed insurance special coupled with those from data mining analyses, such as decision trees or rule generators to detect these schemes PERSONAL INJURY MILLS Many instances have been discovered in which corrupt attorneys and health care providers, usually chiropractors or medical clinics, combine to bill insurance companies for nonexistent or minor injuries. The typical scam includes "cappers" or "runners," who are paid to recruit legitimate or fake auto-accident victims or worker's compensation claimants. [...]
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