Automobile financial credit granting method based on clustering algorithm

Abstract

The invention provides an automobile financial credit granting method based on a clustering algorithm. The automobile financial credit granting method comprises the following steps: constructing a machine learning model for performing model training by adopting a K-means algorithm; inputting input data required by automobile financial credit judgment into the machine learning model; and according to the output data of the machine learning model and a preset customer portrait standard, outputting a loan credit line of a corresponding customer. The automobile financial credit granting method based on the clustering algorithm has the beneficial effect that the automobile financial credit granting method based on the clustering algorithm is provided for performing type subdivision on automobile financial customers based on big data so as to realize accurate examination and approval.

Description

technical field [0001] The present application relates to a credit granting method based on automobile finance, in particular, to a credit granting method of automobile finance based on a clustering algorithm. Background technique [0002] The car loan business is a RMB loan business in which the borrower pays a certain percentage of the down payment when purchasing a consumer car for self-use, and the shortfall is issued by the bank and directly paid to the car dealer. Car loan customers need to provide a large amount of information, which consumes a lot of labor and time costs, and the traditional empirical method of classifying customer categories is highly subjective, and the segmentation results are not objective and lack convincing. And with the continuous growth of the auto market, the bank's auto loan business is growing rapidly. In the face of massive data, the traditional customer segmentation method is even more powerless. Therefore, the application cycle of custo...

AI Technical Summary

Problems solved by technology

Car loan customers need to provide a large amount of information, which consumes a lot of labor costs and time costs, and the traditional empirical method of classifying customer categories is highly subjective, and the segmentation results are not objective and lack convincing

And with the continuous growth of the auto market, the bank's auto loan business is growing rapidly. In the face of massive data, the traditional customer segmentation method is even more powerless. Therefore, the application cycle of customers' auto loans is getting longer and longer, resulting in a decline in customer experience. lower conversion rate

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