Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for realizing credit customer qualification classification based on WOE conversion

A customer and qualification technology, applied in the credit field, can solve problems such as large value range, inaccurate classification of customers with different qualifications, random noise, etc., to achieve the effect of high prediction accuracy

Pending Publication Date: 2019-10-15
梵界信息技术(上海)股份有限公司
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for classifying credit customer qualifications based on WOE conversion, which solves the problem that when there are many customer data, the value range is very large and random noise will be generated. For customers with different qualifications, The classification is not accurate enough to improve the efficiency of manual review to a greater extent, and the problem of high labor costs

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for realizing credit customer qualification classification based on WOE conversion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0026] A method for classifying credit customer qualifications based on WOE conversion, comprising the following steps:

[0027] Step 1. Data preparation and preprocessing. The independent variable data and dependent variable data are divided into five parts, one part is randomly selected as test data, and the remaining four parts are used as training data. WOE conversion and normalization are performed on the training data. And the WOE conversion rule calculated according to the WOE of the training data is applied to the test data. Similarly, the normalization rule of the training data is applied to the test data, and a total of five different combinations of training data and test data are generated accordingly;

[0028] Step 2, model training, input one of the data generated in step 1 into this module, perform feature selection through the lasso feature selection function in this module, and select features useful for customer qualification classification for the next step o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for realizing credit customer qualification classification based on WOE conversion. The method comprises the following steps of data preparation and preprocessing, model training, model evaluation, model deployment, incoming data processing and a customer qualification division module. The invention relates to the technical field of credit. According to the methodfor realizing credit customer qualification classification based on WOE conversion, the noise influence is reduced, meanwhile, the dimensionality of non-numerical data conversion is smaller than thatof ONE _ HOT conversion, and the purposes of automatic model learning, more sensitive customer data change and higher prediction accuracy are realized.

Description

technical field [0001] The invention relates to the technical field of credit, in particular to a method for classifying credit customer qualifications based on WOE conversion. 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. [0003] At present, when there is a lot of customer data, the value range is very large, which will generate random noise. The classification of customers with different qualifications is not accurate enough, and the efficien...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q40/02G06K9/62
CPCG06Q40/03G06F18/2148G06F18/24323
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com