Role Identification of Money Laundering Based on Improved Random Forest

doi:10.3969/j.issn.1006-2475.2018.02.021

Abstract

Abstract: Aiming at the problems of low accuracy and delay of the method of identifying the bank account in the money laundering transaction, the method of public security unit handling, and the existing research based on machine warning, this paper analyzes the trading behavior characteristics of the bank and the clients in the money-laundering crime group, extracting a series of features like the personal background attributes, trade statistics, trading platform, trading behavior to depict outliers from four kinds of view. The features are selected and optimized by random forest model. The annotated data is trained and verified. An application is formed for automatic recognition of traders involved in money laundering. Through the actual data validation, those banking operators with serious hazards can be found out.

Key words: money laundering, role identification, transaction network, centrality, weighted decision tree, random forest

CLC Number:

TP391.1

ZHANG Hao, HUANG Wei, HU Guo-chao. Role Identification of Money Laundering Based on Improved Random Forest[J]. Computer and Modernization, 2018, 0(02): 101-.

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