Abnormal business data screening method for anti-money laundering system

Abstract

The invention relates to a data screening method and particularly discloses an abnormal business data screening method for an anti-money laundering system. The method comprises steps that business data is received; abnormal indexes triggered by the business data are acquired through screening; abnormal models triggered by the triggered abnormal indexes are calculated; business data contained by each triggered abnormal index contained by each triggered abnormal model is derived, and the abnormal business data is acquired. According to the method, the abnormal index and abnormal model screening method is employed, namely combination of the screening method and business scenes, the multiple abnormal indexes satisfying abnormal model triggering conditions are screened in a triggering mode, abnormal transaction possibility of the screened-out business data is substantially enhanced, and the abnormal business data can be more effectively screened. The method is advantaged in that flexible configuration and strong portability are realized, and the method can be applied to different data analysis type systems.

Description

technical field [0001] The invention relates to a data screening method, in particular to a method for screening abnormal business data of an anti-money laundering system. Background technique [0002] In the financial industry, the focus of anti-money laundering work is to analyze customer transaction behaviors and extract information such as various large-value transactions and suspicious transactions for further tracking, investigation and verification. We often select an abnormal indicator to screen out abnormal businesses, but in reality, a transaction / customer / account that meets an abnormal indicator does not necessarily mean that the transaction behavior must be abnormal. In order to further strengthen the accuracy of anti-money laundering monitoring work, It is urgent to propose a more effective method for screening out abnormal transactions. Contents of the invention [0003] In view of this, the present application provides a method for screening abnormal busine...

AI Technical Summary

Problems solved by technology

We often select an abnormal indicator to screen out abnormal businesses, but in reality, a transaction / customer / account that meets an abnormal indicator does not necessarily mean that the transaction behavior must be abnormal. In order to further strengthen the accuracy of anti-money laundering monitoring work, There is an urgent need to propose a more effective method for screening out abnormal transactions

Images