A credit decision-making data optimization extraction system and method

Abstract

This application discloses a credit decision-making data optimization extraction system and method. The system includes: a user management module, which is used to perform decision-making optimization management on corresponding decision-making account information accessed by decision-making optimization users, and collect corresponding decision-making optimization user data; The decision-making data overview module is used to view the decision-making optimization user data and convert it into relevant information data of the first decision-making data set and the second decision-making data set; the decision-making log acquisition module is used to collect the collected first and second decision-making data Extract the log information from the set decision data and check its operation status, and perform noise reduction processing on the log information by filtering; the decision data optimization module is used to allow decision optimization users to create or edit the first and second decisions through the decision data editing function Data set, and optimize the effectiveness of the edited first and second decision data sets. The application can optimize the credit decision data and improve the accuracy of the credit decision data.

Description

technical field [0001] This application relates to the field of computer technology, in particular to a system and method for optimizing and extracting credit decision data. Background technique [0002] With the tide of Internet finance and the explosive growth of various businesses, facing many challenges such as imperfect personal credit system, malicious fraudulent loans, bad debts and overdue debts, and high debt recovery costs, credit decision-making has always been the top priority of business. However, there are many problems such as the high cost of building a credit decision system, the serious shortage of professional credit decision-making talents, the low efficiency of manual review, the cumbersome process, and the inability to freely combine and configure credit decision-making rule components. issues that people care about. [0003] As the industry environment continues to change, especially fraudulent gangs gradually showing the characteristics of grouping, ...

AI Technical Summary

Problems solved by technology

[0005] Traditional credit decision-making decisions are written in the code. Since the credit decision-making rules need to be continuously iterated and optimized, when iterating and optimizing the credit decision-making rules, the credit decision-making personnel often write the rules first, and then the IT personnel will target the credit in the background. Rules written by decision makers perform code iteration and verification. This method of developing rules into the system through IT personnel needs to connect a large amount of internal data, and data integration is complicated, which makes IT personnel overwhelmed, time-consuming and labor-intensive, and has a long iteration cycle. Poor system flexibility and adaptability

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