A penetrating type full-transparent high-efficiency real estate network borrowing information intermediary platform system

Abstract

The invention relates to a penetrating type full-transparent high-efficiency real estate network borrowing information intermediary platform system. The system comprises a pre-loan one-key rating andvaluation credit granting system, a pre-loan remote risk control system, a post-loan live broadcast processing system and a VIP client classified cultivation system. The credit evaluation method comprises a set of advantages of all systems: the credit of the borrowing subject is judged in an omnibearing manner through big data, and a high-objective-degree credit evaluation conclusion can be obtained. The reference can be truly obtained for evaluating the big data information valuable to the credit of the user; the rule engine of the credit granting process is clear and is not easily influencedby subjective factors, the possibility of dark box operation is avoided, and the certainty of the rating credit granting process is high; risk control is completed through remote investigation, and the risk control cost is low. The post-loan real-time live broadcast discussion group realization system can perform live broadcast display on various items subjected to post-loan processing in real time, in a full-transparent, efficient and objective manner to a lender, and is convenient for the lender to supervise; the VIP client classified cultivation system achieves targeted classified management and is high in management efficiency.

Description

technical field [0001] The invention relates to a penetrating fully transparent and efficient real estate network lending information intermediary platform system, and relates to the technical field of network lending information management. Background technique [0002] Real estate lending refers to a loan model in which the borrower registers real estate mortgages in the lender's name, the lender lends funds to the borrower, and the borrower repays the principal and interest to the lender in the manner agreed in the contract. [0003] With the further deepening of domestic financial reforms and the further opening up of financial market access, market competition has become increasingly fierce. Whether it is banks, small loan companies, pawn shops, guarantee companies, online lending information intermediary agencies, financial leasing companies, private lending institutions, natural persons, enterprises and other organizations and other financial direct credit entities, o...

AI Technical Summary

Problems solved by technology

Big data information that is really valuable for evaluating user credit is likely not to appear in the application materials;

[0005] (2) The credit investigation is done manually and the process is long

The method of manually verifying these materials is very time-consuming and laborious. It takes several months, and the efficiency is low, which greatly affects the borrowing efficiency of the borrower.

[0006] (3) The credit granting process is relatively difficult, manual judgment requires rich experience, and is easily affected by subjective factors, which may easily lead to greater uncertainty in the rating credit granting process

[0009] (1) Risk control is completed by manual on-site investigation, the process is long and the cost of risk control is high;

[0010] (2) The risk control process is difficult, manual judgment requires rich experience, and is easily affected by subjective factors, which may easily cause greater uncertainty in the risk control process

[0011] The disadvantages of the post-loan processing process in the existing technology include: the post-loan processing is done manually offline, the process is long, and the progress of the post-loan processing cannot be transparently displayed to the lender in time, and the lender cannot timely and efficiently Supervise borrowing matters to ensure the safety of your own funds

[0012] The existing technology lacks a VIP customer classification and cultivation system, and the classification and grading management of customers is poorly targeted. The user habits of various customers are not analyzed through big data, and the management efficiency is low.

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