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Machine learning interpretability-oriented credit default prediction method and system

A technology of machine learning and forecasting methods, which is applied in the direction of machine learning, forecasting, error detection of redundant data in calculations, etc., can solve problems such as inaccurate evaluation of credit, and achieve high efficiency, accurate results, and accurate predictions high rate effect

Pending Publication Date: 2021-04-20
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to solve the shortcomings in the prior art that credit cannot be accurately evaluated, and propose a credit default prediction method and system oriented to machine learning interpretability

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  • Machine learning interpretability-oriented credit default prediction method and system
  • Machine learning interpretability-oriented credit default prediction method and system
  • Machine learning interpretability-oriented credit default prediction method and system

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Embodiment Construction

[0025] The following description serves to disclose the present invention to enable those skilled in the art to carry out the present invention. The preferred embodiments described below are only examples, and those skilled in the art can devise other obvious variations.

[0026] A credit default prediction method oriented to machine learning interpretability, comprising the following steps:

[0027] S1. Data collection. The sources of collected data include business statistical data, credit data provided by banks and big data provided by third parties;

[0028] S2. Data preprocessing: The cleaning and screening module cleans the input data. If a variable in the data is missing, a small number of non-core data will be deleted. If the deleted amount is too large, the overall distribution sampling method and Fill in the data by means of maximum likelihood estimation based on other information;

[0029] S3. Data division and training: divide the cleaned data into multiple group...

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Abstract

The invention relates to the technical field of credit default prediction, in particular to a machine learning interpretability-oriented credit default prediction method, which comprises the following steps of S1, acquiring data; s2, preprocessing the data; s3, dividing and training data; s4, verifying the model. The invention further discloses a system of the credit default prediction method oriented to machine learning interpretability, the system comprises a data acquisition module, the data acquisition module is connected with a cleaning and screening module through a signal line, the cleaning and screening module cleans the input data, and if a certain variable of the data is missing, a few non-core data is deleted; if the deletion amount is large, the data is filled by an overall distribution sampling method and a method of performing maximum likelihood estimation according to other information, and the cleaning and screening module is connected with a data division module through a signal, so that default prediction can be performed efficiently and accurately.

Description

technical field [0001] The invention relates to the field of credit default prediction, in particular to a credit default prediction method and system oriented to machine learning interpretability. Background technique [0002] With the maturity of the financial lending market, small and micro enterprises have an increasing demand for loans. At the same time, the requirements for loan approval efficiency, loan issuance time, and loan issuance management continue to increase. Under the current conditions, how to save audits? Time, improvement of audit accuracy, and loan pool optimization management have become huge challenges. How to scientifically and reasonably price various risks is the key to bank credit departments to achieve efficient operation management, reduce operating costs, and ensure customer service quality and level. important link. At present, there is no good evaluation standard for customer credit review, and misjudgment is prone to occur. Contents of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/02G06F16/215G06F16/23G06F11/14G06N20/00
Inventor 吴金迪
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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