Method and system for monitoring network behavior data

Abstract

The invention provides a method and system for monitoring network behavior data. The method particularly comprises the steps of: obtaining network behavior data of a user from an online application program periodically or regularly, and regarding the network behavior data as historical network behavior data for collective storage; determining a corresponding risk model based on the historical network behavior data of the user; calling the collectively stored historical network behavior data for historical risk identification based on the definition of the risk model, and regarding a corresponding historical risk identification result as a parameter of the risk model for storage; obtaining the online network behavior data of the user in real time; performing online risk identification on the online network behavior data based on the risk model so as to obtain an online risk identification result; and processing a credit object associated with the user based on the online risk identification result. The method and system for monitoring the network behavior data can improve timeliness and accuracy of credit risk monitoring.

Description

technical field [0001] The present application relates to the technical field of computer networks, in particular to a method and system for monitoring network behavior data. Background technique [0002] Credit business is the main business category of banks, one of the pillar businesses for banks to obtain profits, and one of the focuses of market competition among commercial banks. However, the credit business is also a risky business. Therefore, timely and accurate monitoring of credit risks to improve asset quality and reduce the proportion of non-performing assets is an important link for commercial banks to improve their own viability and competitiveness. [0003] At present, a typical monitoring method for corporate credit risk in the existing technology is to collect corporate operating data and risk-related information data through visits by bank user managers, and enter the data into the database system after the collection is completed. Finally, through analysis...

AI Technical Summary

Problems solved by technology

[0005] 1. The entire process requires manual intervention, which undoubtedly increases labor costs;

[0006] 2. The method of regular monthly visits by the user manager makes the collection, input and analysis of information data static. In this way, during the analysis, the credit risk of the enterprise has often occurred or been exposed, so it has the disadvantages of being untimely and lagging;

[0007] 3. Since the collection, input and analysis of information data are all done manually, in particular, the risk measurement is a qualitative analysis of information data, which is mainly completed by the analyst’s intuition and experience, so it has the disadvantages of being subjective and inaccurate

[0009] The above-mentioned monitoring method for personal credit card credit risk conducts a comparative analysis of the transaction data that has occurred. Although the analysis of the transaction data can be used to obtain information related to the risk such as the total amount of personal user’s monthly consumption and whether the repayment is timely; however, The transaction data has no rules to follow, and it is impossible to analyze the consumption habits, consumption preferences and other behavior characteristics of individual users based on the transaction data alone. In this way, if a user has the habit of luxury consumption, but The user's salary is far less than the total amount of monthly consumption. Even if the individual user has no bad record of overdue repayment, his credit card credit still has a great risk

The above-mentioned monitoring method is also more focused on the monitoring after the loan, so the risk in this situation cannot be monitored, that is, the above-mentioned monitoring method for personal credit card credit risk also has the disadvantages of untimely and inaccurate monitoring

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