Dynamic high-risk customer-group detection method and system

Abstract

The invention provides a dynamic high-risk customer-group detection algorithm. The algorithm is used for detecting risk degrees of new users belonging to different customer groups, and includes the following steps: i, using a clustering algorithm to build the groups for multiple historical users on the basis of multiple pieces of historical data of the multiple historical users to form the multiple customer groups, wherein the clustering algorithm is related to types of the historical data; a, determining risk degrees of all the customer groups on the basis of an evaluation index, and determining that the customer groups of which the risk degrees are greater than a risk threshold value are high-risk customer groups; and b, determining similarity degrees between the new users and the high-risk customer groups on the basis of a distance function, and determining that the new users of which the similarity degrees are greater than a first similarity degree threshold value are risk users, wherein the distance function is related to types of user data of the new users. The method has the advantages that the method is simple to operate, and convenient to use. The invention provides the dynamic risk customer-group detection algorithm and a corresponding system, which have extremely high commercial values.

Description

technical field [0001] The invention belongs to the field of risk detection, and in particular relates to a dynamic high-risk customer group detection method and system. Background technique [0002] In today's era of rapid development of virtual economy, the credit system and its risk management are increasingly concerned. [0003] Promoting the use of personal credit for consumption is an important method to expand domestic demand and promote economic development in today's social and economic environment. At present, China's first task is to vigorously develop the economy, and use personal credit consumption to play a role in promoting national economic growth. However, personal credit There will be many difficulties and obstacles in the development of personal credit, and it is difficult to effectively control personal credit. With the development of financial securitization, banks have transformed from a seller's market to a buyer's market. Customers with strong capabi...

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Problems solved by technology

[0003] Promoting the use of personal credit for consumption is an important method to expand domestic demand and promote economic development in today's social and economic environment. At present, China's first task is to vigorously develop the economy, and use personal credit consumption to play a role in promoting national economic growth. However, personal credit There will be many difficulties and obstacles in the development of personal credit, and it is difficult to effectively control personal credit. With the development of financial securitization, banks have transformed from a seller's market to a buyer's market. Customers with strong capabilities are the main competition of commercial banks. When developing markets for credit marketing, investigators accept lenders' applications, evaluate their credit based on lenders' information, and at the same time check the legitimacy, safety, and profitability of loans. Conduct surveys, determine loan risk, and submit analysis reports

[0004] However, when the traditional model evaluates the credit risk of the lender, the cycle is often too long due to the review of various materials, which seriously affects the efficiency, and the authenticity of all the materials submitted by the lender cannot be guaranteed, resulting in many high risks. The crowd obtained the loan successfully due to the submission of false information and certificates or due to the negligence of the evaluation and review staff. Once the loan cannot be repaid on time, it will cause huge losses to the bank or lending unit

[0005] At present, the customer credit algorithm based on big data is recalculated for each new user, that is, the credit algorithm must be repeatedly executed for each new user, which brings about a high amount of data processing and slow processing, so , the existing technology does not provide a relatively fast and accurate algorithm for detecting the risk of new users

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