Abstract:

In recent years,with the rapid development of FinTech,digital finance has flourished and brought huge positive effect on society.Meanwhile,new risks have been introduced into banks.For example,the black production related to network security has experienced explosive growth,and telecommunication network fraud has caused property losses to the public.In the era of digital finance,the commercial banks have not only ushered in new opportunities and dynamics,but also faced new challenges and requirements for digital transformation.As a result,e-finance has become a new battlefield.With this context,a real-time anti-fraud machine learning model based on high-dimensional transaction behavior portrait through enhanced RFM feature-derivation and machine learning modeling was established in this paper.Relying on the new technologies such as big data,stream computing,a model application solution to real-time risk control was formed including systematic deployment,model application strategies and iterative model optimization.Through practical observation,the AUC of the model reaches 0.972,which provides a keen insight into fraud risk,realizes millisecond-level risk identification,and promotes risk control ability of e-finance significantly.

Key words: digital finance, machine learning, anti-fraud, RFM, stream computing