The Research On The Application Of Data Mining In Anti-Money Laundering Field

Data Mining(DM for short) is becoming a hotspot in Artificial Intelligence and Database domain. So far, research on DM has covered several domains and techniques, such as time sequence rules, association rules, and classification rules. With the rapid development of Internet, Web DM is also becoming a new hot research field.This dissertation focuses on the application of DM in Anti-Money Laundering.Banks have accumulated a mass of transfer records since they used computer to deal, but those records are mainly used in simple query and statistic. Application of DM in it is limited on Customer Relation Management, Financial Market Analysis, etc. Money laundering is a grievous problem that financiers and officers meet now. It is a kind of sever crime and appeared more and more rampant these years. Present techniques against money laundering are still out of date and they mostly depend on human to look for suspicious accounts by experience. There has not appeared any DM method used in it.This dissertation aims at application of DM in anti-money laundering. A money-laundering risk rating model is explored. Research on automatic anti-money laundering is wished to progress.Firstly, this dissertation has introduced development courses and development trends of the DM technology briefly. Then from the technological analysis angle we have made a comprehensive assessment of the main DM technology—decision tree classification, and mainly discussed with representative algorithm ID3. We pointed out and analyzed the advantages and lacks for this algorithm. Then we use this algorithm to design and realize the money-laundering risk rating model. Through what we have mined, we obtained the rule set to rate the money laundering risk. Finally, we pointed out the direction of further research.