A risk control method and system for credit big data

Abstract

The invention discloses a credit big data-oriented risk control method and system thereof. The terminal equipment completes data collection, and the server completes data fusion, cleaning, and labeling of attribute tags and subject tags to realize data preprocessing; The risk control module is designed for the original data collected by the terminal. By labeling the data and dividing the sensitivity, the data is divided into different sensitive levels to achieve first-level risk control; and then for the initial credit of the users of the data storage platform, through Analyze the subject tags in the original data, evaluate the initial trust and authority of data visitors, and achieve the purpose of secondary risk control; finally, for the problem that the system and storage platform need to monitor the behavior of data visitors in real time, dynamically define according to the behavior of visitors Subject trust, so as to complete the three-level risk control for credit big data.

Description

technical field [0001] The invention belongs to the field of security technology, and in particular relates to a credit big data-oriented risk control method and a system thereof. Background technique [0002] With the continuous development of Internet technology, the importance of data credibility issues has gradually become more prominent, and the private information contained in credit big data is also increasing day by day. Due to the wide variety of credit big data and multi-source heterogeneity, the effect of credit big data risk control methods is often unsatisfactory. Therefore, how to design a risk assessment method with high security, reliability and systematic important. [0003] Generally speaking, the reasons for the low risk control effect of credit big data can be roughly divided into the following three aspects: First, because the credit data collected by different terminals is multi-source heterogeneous data, the amount of information is large but the qual...

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Problems solved by technology

[0003] Generally speaking, the reasons for the low risk control effect of credit big data can be roughly divided into the following three aspects: First, because the credit data collected by different terminals is multi-source heterogeneous data, the amount of information is large but the quality is not good, which may lead to The validity of the obtained credit data is not high, and it cannot well meet the data requirements of risk control; secondly, the existing risk control system generally has partial control phenomena, which is not systematic, and cannot predict and control risks from an overall perspective ;Finally, due to the malicious falsification of the collected data visitors' data, these data will lead to inaccurate measurement of trust, resulting in non-standard setting of access rights, which will seriously affect the effect of credit big data risk control

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