A credit risk assessment model generation method based on machine learning and related equipment

Abstract

The invention relates to the field of machine learning, and discloses a credit risk assessment model generation method based on machine learning and the related equipment. The method comprises the steps of obtaining loan sample data of a user; counting preset sample data stored in the loan sample data; when the number of preset sample data stored in the loan sample data reaches a preset thresholdvalue, taking the loan sample data as target training data; and converting the target training data into a loss function, and adding the loss function into a preset credit risk assessment model for training, thereby generating a target credit risk assessment model. According to the invention, by pre-judging the loan sample data through the preset sample data; and training the pre-established credit risk assessment model by using the judged effective loan sample data so as to generate a target credit risk assessment model, and training the machine model by using the effective sample data, the iteration period of the machine model is shortened.

Description

technical field [0001] The invention relates to the technical field of machine models, in particular to a machine learning-based credit risk assessment model generation method and related equipment. Background technique [0002] Machine learning is a method for terminal equipment to use data to perform various tasks. Through machine learning, the input data can be analyzed, the laws in the data can be summarized and a machine model can be generated, so as to realize the analysis and judgment of the data. However, the judgment Whether the generated machine model is accurate or not needs to be obtained after a long period of performance, thus increasing the iteration cycle of the machine learning model. Contents of the invention [0003] The main purpose of the present invention is to propose a machine learning-based credit risk assessment model generation method and related equipment, aiming at shortening the iteration cycle of the machine model. [0004] To achieve the ab...

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Problems solved by technology

[0002] Machine learning is a method for terminal equipment to use data to perform various tasks. Through machine learning, the input data can be analyzed, the laws in the data can be summarized and a machine model can be generated, so as to realize the analysis and judgment of the data. However, the judgment Whether the generated machine model is accurate or not needs to be obtained after a long time of performance, thus increasing the iteration cycle of the machine learning model

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