Research On The Anti-money Laundering Regulatory Application System And The Application Of DBSCAN Algorithm

Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains from predicted criminal offenses,such as drug crimes,mafia organized crimes,smuggling,terrorist crimes,financial fraud,gambliing crime,corruption and bribery,tax crime,illegal fund-raising,pyramid schemes,destruction of the financial management order crime,and so on.In recent years,with the development of people's living standards and economic level,more and more money laundering phenomenon becomes rampant.Money laundering crime is connected with a variety of predicated offense crimes,so it is harmful to the stability of country's political,and harmful to the economic growth and the harmonious of people's daily life,and even the threaten to the international community's security.Thus,anti-money laundering plays a more and more important role in the stability of country's political,the stability of whole economic and social harmonious.In order to effectively carry out anti-money laundering work,this paper designs and implements the anti-money laundering regulatory application system(AMLRAS),which can automatically analyze and summary the money laundering cases and predicated offense crime cases,at the same time,it can collect,analyze and statistic the large cash transaction data and high suspicious transaction data in detail.The system offers specificity,comprehensive,precised and authoritative case querying platform for anti-money laundering work by mining the value of the existing cases.It will become an information service platform in training,researching and investigating of anti-money laundering daily work.As the crime income obtained by a variety of illegal financial fraud,such as financial fraud,money laundering,tax evasion,insider trading and so on,becomes more and more rampant than traditional crime obtain,for example,robbery,theft and other direct possession of wealth through violence.Besides,the cash transactions have a close relationship with financial crimes,so the strict verification of cash transaction data is very important to prevent the financial crimes occur.To take full advantage of those large cash transaction data in AML regulatory application system,this paper also adopt data mining techniques DBSCAN clustering algorithm to identify suspicious financial transactions,while using link analysis(LA)to mark the suspicious level.The presumptive approach is tested on large cash transaction data which is provided by a bank where AMLRAS has already been applied.The test result analyzed by expert,it proves that this method reduce the work in detecting suspicious financial transaction cases from mass financial data,which is helpful to prevent money laundering from occurring and helpful to open the anti-money laundering regulatory work effectively.