Target object evaluation method and related equipment based on multi-scorecard fusion

Abstract

The application belongs to the field of artificial intelligence, and relates to a target object evaluation method based on multi-score card fusion and related equipment thereof. The method includes: performing data cleaning according to the distribution state of the characteristic variables included in the historical data of the target object, and then performing characteristic variable screening Obtain the original feature variable set; perform grouping operation on the original feature variable set to obtain multiple mutually exclusive target feature variable sets; construct multiple target LR models based on each target feature variable set and generate model accuracy values; generate For multiple scorecard models, the scorecard fusion is performed according to each scorecard model and its corresponding model accuracy value to obtain the target scorecard model, and the target score value can be output based on the target scorecard model. In addition, the present application also relates to blockchain technology, and data determined to be private information can be stored in the blockchain. This application can reduce the risk of the model caused by the shift of characteristic variables, improve the stability of the model and ensure the accuracy of the model.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and specifically relates to a target object evaluation method, device, computer equipment and storage medium based on the fusion of multiple scorecards. Background technique [0002] Scorecard models are widely used in risk assessment and control in the financial industry, such as pre-loan application scorecards, loan behavior scorecards, post-loan collection scorecard models, etc. During the training process of the scorecard model, it is necessary to obtain the characteristic variables as the model input. On the one hand, when obtaining the characteristic variables, the characteristic variables need to be screened to reduce the information redundancy of the characteristic variables. On the other hand, the model fitting process is highly correlated. Feature variables will be partially eliminated, which will lose the diversity of features to a certain extent, thereby redu...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the embodiment of the present application is to propose a target object evaluation method, device, computer equipment, and storage medium based on multi-scorecard fusion, so as to solve the problem of the accuracy of the scorecard model caused by the loss of feature diversity due to the elimination of feature variables in the prior art. decrease, and the problem of reduced stability of the scorecard model due to the long time span of the target variable

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