Enterprise credit data co-processing method and system based on intelligent contract

Abstract

The invention belongs to the technical field of enterprise credit risk assessment, and particularly relates to an intelligent contract-based enterprise credit data co-processing method, system and platform. Through the method recorded in the scheme and the system and the platform corresponding to the method, the enterprise credit data is subjected to uplink sharing through a dynamic asymmetric encryption algorithm, the privacy of the data is effectively protected, and the worry of data leakage of all parties is eliminated. The intelligent contract automatically completes reading and calculation of enterprise credit data, the whole process is not interfered by any party, and the right of each participant to obtain a result and the obligation of providing data are guaranteed; the enterprise credit evaluation results obtained by all parties are consistent, and the quality of the results can be obtained according to the quality of the data provided by any party, so that the motivation of all parties for improving the high-quality data can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of enterprise credit risk assessment, and in particular relates to a method, system and platform for collaborative processing of enterprise credit data based on a smart contract. Background technique [0002] Enterprise credit risk assessment is the basis for enterprise classification and supervision and precise enterprise services. According to the results of enterprise credit risk assessment, it can provide the basis for market activities such as financial credit, bidding and bidding, and business cooperation, as well as market supervision, inspection and law enforcement. Management provides support. At present, the credit risk assessment of enterprises relies on the expert method and the big data method. The expert method generally formulates the evaluation model in the mode of credit score. Once the model is basically unchanged, it has greater subjectivity; the big data method generally designs the model...

AI Technical Summary

Problems solved by technology

[0004] The traditional enterprise credit evaluation is a calculation mode in which each data owner "does things independently". Each party uses its own enterprise credit data to establish an independent credit evaluation model to calculate the credit status of the enterprise in this field, but this method cannot comprehensively evaluate The credit status of an enterprise is very one-sided, and sometimes there are even contradictions where one party evaluates the credit is good, while the other party evaluates the risk.

Of course, the evaluator sometimes also refers to the credit evaluation results of other parties, but it is difficult to comprehensively evaluate the actual credit situation of a company because of the inability to grasp the evaluation model of the other party; figure 1 shown

[0005] That is to say, the traditional enterprise credit evaluation method is one-sided and has many problems. It cannot effectively use the credit data of all parties for collaborative calculation, and the credit analysis model lacking the data support of all parties is also difficult to be accurate.

The method of comprehensively evaluating credit results by sharing credit results is also unscientific. On the one hand, it is difficult to determine the weight of each party’s result data, and on the other hand, it is difficult to guarantee the timeliness and accuracy of the credit evaluation results shared by all parties.

[0006] 1) The enterprise credit information model established for single-sector data cannot reflect the overall credit situation of the enterprise, and the lack of data dimensions will suffer from the one-sidedness of the credit evaluation results;

[0007] 2) When synthesizing the calculation results of all parties, it is difficult to establish a scientific and reasonable comprehensive calculation model due to the lack of data understanding;

[0008] 3) All parties hope to obtain the other party's credit evaluation result data, but they often "cut corners" on the data provided by themselves, and it is difficult to guarantee the timeliness and accuracy of the data in an environment of mutual distrust

Images