CREDIT MEASUREMENT OF RURAL INTERNET CONSUMER FINANCE BASED ON BLOCKCHAIN CLUSTERING AND FUSION MODELS

Abstract

This paper focuses on the empirical analysis of personal credit assessment of online lending platform from the perspective of personal credit, and the security of credit privacy data can be guaranteed by blockchain classification model. This paper is mainly based on the chain security encryption operation and decentralized data classifier training model, blockchain storage credit data between the ecological nodes through the transmission of transaction decision data return beacons, to achieve the data retrieval, use, confirm the rights and rewards, and at the same time the use of clustering learning algorithms combined with the decentralized training model to build a unique algorithmic training system, through the machine learning to backtrack all the transaction records, the sharing of the After data processing of credit data information, the fiducial correction fitting model using feedback from data samples, thus opening the modeling method of blockchain and clustering algorithm combined application in the field of credit. In the final analysis, the research on the application of blockchain technology in the credit collection industry should not stop at guaranteeing the security and traceability of data, but rather apply the "pre-credit review", "credit monitoring" and "post-credit management" to the entire credit collection industry. "Instead, it should be applied to the entire credit collection process, and used to guide Internet credit bureaus in their daily credit collection activities. Blockchain technology mainly solves the problem of credit trust and security, for this reason, it is necessary to construct a complete set of methods for analyzing, verifying and measuring Internet credit data. This paper combines the blockchain and the clustering algorithm in machine learning, and empirically analyzes the credit data of Internet consumer financial institutions under this framework.