Suspicious Money-laundering Activity Research Based On XX Bank’s Customer Transactions

Money laundering crime seriously affects the healthy development of the financial stability and economic, to fight money laundering crime is an important problem facing the world. From the number and amount of money laundering crime, financial institutions, especially the commercial Banks has become the forefront of anti-money laundering. Therefore, give full pay to the main force of commercial Banks anti-money laundering, build solid anti-money laundering defenses, is not only an effective way in preventing and controlling, but also the key to crack down on the crime of money laundering activities. How to efficiently identify suspicious financial transactions from the vast amounts of financial transaction data is the key factor, which can smoothly carry out the key problems. Data mining can be extracted from huge amounts of data and dig out the useful knowledge, and clustering analysis is an important branch of data mining research and application of anti-money laundering by the governments of the relevant departments attach importance to it increasingly. Nowadays, using the data mining technology at home and abroad, such as cluster analysis is used to identify the suspicious transactions automatically has been widely used.I major in the banking software development and testing, and involved in money laundering software from 2007. I witnessed the process of anti-money laundering work from scratch to further development in our country. Anti-money laundering software developed by our company successively applied in Sumitomo Mitsui Banking Corporation, BNP Paribas, and foreign small commercial banks, etc. With the development of the banking business, the money laundering software need to update, the identification framework and application of suspicious transactions is too simple, easy to evade, the adaptive ability is low, data processing ability and data volume growth does not match, and other issues. This paper on the basis of the research literature at home and abroad for reference and experience, combined with the actual situation of China’s anti-money laundering work and the characteristics of the suspicious financial transactions, chose the CURE algorithm in data mining, and with the help of XX bank in 2014-2015 real foreign exchange trading data proved the effectiveness of the algorithm. Effective development of anti-money laundering work requires not only commercial banks from the system to the staff at all levels, and the importance of strengthening its internal control, on the other hand, rely on the computer technology to build suitable for their own situation, performance, more powerful suspicious information identification method and anti-money laundering software system is also essential. The small foreign commercial banks, like XX bank, in the process of identification of suspicious transactions should pay special attention to the background information of the customer, and the characteristics of the industry, improve the quality of suspicious transactions report.