Credit customer qualification classification method based on WOE conversion through machine learning

A technology of machine learning and classification methods, applied in the field of credit customer qualification classification, can solve the problems of high labor costs and low efficiency of manual review, and achieve the effect of reducing the impact of noise and facilitating understanding

Pending Publication Date: 2019-10-11
梵界信息技术(上海)股份有限公司
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Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a credit customer qualification classification meth

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  • Credit customer qualification classification method based on WOE conversion through machine learning

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Embodiment Construction

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0026] see figure 1 , the present invention provides a technical solution: a credit customer qualification classification method based on WOE conversion through machine learning, including:

[0027] Data preparation and preprocessing module: including application data, credit data, call records and repayment data, through data cleaning and segmentation and WOE conversion to achieve data normalization;

[0028] Model training and evaluation module: including l...

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Abstract

The invention discloses a credit customer qualification classification method based on WOE conversion through machine learning. A system comprises a data preparation and preprocessing module, a modeltraining and evaluating module, a model deployment module, an inlet data processing module and a client qualification division module. The data preparation and preprocessing module is used for calculating original data I from the application data, the credit investigation data and the call record, calculating original data II through the customer category and the repayment data, and carrying out data preprocessing on the original data I and the original data II. The invention relates to the technical field of qualification classification. The credit customer qualification classification methodbased on WOE conversion through machine learning provides the system for realizing customer classification with different qualifications based on the machine learning method, the workload of manual auditing can be reduced, the approval efficiency is improved, learning is performed in time according to newly added customer information, self-adaption to customer qualification change is realized, the manual auditing efficiency can be improved to a greater extent, and the labor cost is reduced.

Description

technical field [0001] The invention relates to the technical field of qualification classification, in particular to a credit customer qualification classification method based on WOE conversion through machine learning. Background technique [0002] With the development of the credit industry, there are more and more loan applications for lending institutions. The traditional review method is a combination of manual review and scorecard. The traditional method is inefficient and not sensitive enough to changes in customer data. Therefore, a system that automatically learns according to customer changes and assists manual review is needed to improve approval efficiency and optimize the approval process. In addition, digging deep into customer information is helpful to expand the customer base. Contents of the invention [0003] (1) Solved technical problems [0004] Aiming at the deficiencies of the prior art, the present invention provides a credit customer qualificat...

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Application Information

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IPC IPC(8): G06Q40/02G06K9/62
CPCG06Q40/03G06F18/24G06F18/214
Inventor 李鹏慧侯李伟赫汗笛胡书瑞李江
Owner 梵界信息技术(上海)股份有限公司
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