Name
Abstract | ||
|---|---|---|
Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains, to make it legalization. In this paper, we design and implement the anti-money laundering regulatory application system (AMLRAS), which can not only automate sorting and counting the money laundering cases in comprehension and details, but also collect, analyses and count the large cash transactions. We 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 result proves that this method is automatable to detect suspicious financial transaction cases from mass financial data, which is helpful to prevent money laundering from occurring. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CyberC.2014.89 | CyberC |
Keywords | Field | DocType |
aml regulatory application system,pattern clustering,financial data,amlras,presumptive approach,link analysis,la,financial data processing,antimoney laundering regulatory application system,link analysis (la),suspicious financial transaction identification,transaction processing,bank,money laundering,sorting,data mining techniques,dbscan clustering algorithm,data mining,money laundering, aml regulatory application system, dbscan clustering algorithm, link analysis,cash transactions | Data mining,Link analysis,Computer security,Computer science,Sorting,Financial transaction,Cluster analysis,Transaction data,Money laundering,DBSCAN,Cash | Conference |
ISBN | Citations | PageRank |
978-1-4799-6235-8 | 0 | 0.34 |
References | Authors | |
4 | 5 | |