Transaction account risk management method based on block chain

Abstract

The invention discloses a transaction account risk management method based on a block chain. The method comprises the following steps: developing three data sequences in a transaction account, namely a transaction asset sequence, a transaction direction sequence and a transaction quantity sequence, and carrying out the distributed storage through the block chain. When a new transaction behavior occurs in a customer transaction account, corresponding transaction assets, transaction directions and transaction quantity enter three data sequences to generate a new data block, and finally risk evaluation of the transaction action is completed through a consensus mechanism and a verification mechanism of the block chain. Evaluation contents comprise common abnormal behaviors in transactions such as large amount, splitting, false report, stealing and the like.

Description

Technical field: [0001] The invention relates to the field of securities transaction risk management, in particular to a method for managing securities transaction account risks by applying block chain technology. Background technique: [0002] In the field of asset management, account risk management involves regulatory analysis and compliance review of transaction-related laws, rules and guidelines. The transaction account system needs to strictly implement the internal control process from data analysis, technology development to product. By reviewing each transaction record, the due diligence of transaction behavior is completed, which is an important step to avoid the risk of violation of laws and regulations in the transaction. [0003] The existing transaction account analysis technology mainly focuses on manual query of historical transaction records and subjective judgment on matching suspicious transaction information, and cannot perform automatic correlation analy...

AI Technical Summary

Problems solved by technology

[0003] Existing transaction account analysis technology is mainly based on manual query of historical transaction records and subjective judgment on suspicious transaction information matching, and cannot perform automatic correlation analysis on all transaction behaviors in real time, accurately and efficiently

Due to the large number of trading accounts and the existence of transaction records in multiple accounts, traditional technical methods are difficult to effectively identify violations and illegal activities in these complex scenarios, and the automation and intelligence of account risk management has been at a relatively low level

Images