Multilayer fund abnormal flow direction monitoring method based on knowledge graph

Abstract

The invention discloses a multilayer fund abnormal flow direction monitoring method based on a knowledge graph. The method comprises the following steps: preprocessing transaction flow data within a period of time; extracting an account set involved in the transaction flow; marking different labels on accounts in the set, carrying out aggregation processing on transaction flow data, displaying theaccumulated transaction amount and accumulated transaction times of the main account and the transaction opponent account within a period of time, wherein the knowledge graph operation performance isgreatly improved, and the fund flow direction of the account main body within a period of time is clearly displayed; and finally, importing the processed data into a knowledge graph, and identifyingthe multi-layer fund circulation condition of the account through a graph rule so as to find a suspicious account set; and displaying a suspicious account fund flow direction through a knowledge graphvisualization function. The knowledge graph rule is used for monitoring the abnormal flow direction of the multilayer funds in the field of bank case prevention for the first time, the suspicious account of the abnormal flow direction of the multilayer funds can be effectively recognized, and the visual technology is used for assisting verification and check.

Description

technical field [0001] The invention belongs to the field of capital flow analysis, and in particular relates to a multi-layer abnormal capital flow monitoring method based on a knowledge map. Background technique [0002] In recent years, my country's financial operational risk cases have shown a trend of frequent and high incidence. Many banks have successively exposed operational risk cases involving huge amounts of money. Cases related to bank business operations have occurred frequently, and the cases in terms of operational risk have become increasingly complex. The method is secretive. Operational risk cases occur frequently, causing huge economic losses and social impact, and bringing new challenges to the stable operation of the banking industry. How to effectively prevent and control cases in different scenarios in the banking industry has become an urgent problem to be solved. For financial operational risk cases, banks generally analyze the case characteristics ...

AI Technical Summary

Problems solved by technology

The traditional capital flow analysis of banks uses SQL processing rules to describe the capital flow between accounts. The processing process is long, the processing logic is complicated, and the processed rules cannot accurately describe the characteristics of capital flow. There are a series of problems, such as 1 ) The development efficiency is low, especially the implementation of the rules for identifying complex capital exchange relationships is quite complicated; 2) The accuracy is low, the SQL rules are not fine-grained enough, and the capital flow relationship of the account cannot be accurately identified; 3) The identification is incomplete, due to the uncertainty of the capital flow nature, unable to fully describe the multi-layered fund flow of the account

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