AU2019101182A4 - Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning - Google Patents

Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning Download PDF

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Publication number
AU2019101182A4
AU2019101182A4 AU2019101182A AU2019101182A AU2019101182A4 AU 2019101182 A4 AU2019101182 A4 AU 2019101182A4 AU 2019101182 A AU2019101182 A AU 2019101182A AU 2019101182 A AU2019101182 A AU 2019101182A AU 2019101182 A4 AU2019101182 A4 AU 2019101182A4
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Australia
Prior art keywords
model
data
lending
risk assessment
unsupervised learning
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AU2019101182A
Inventor
Yawen Feng
Mengdi HAN
Fangming Huang
Xiuran Li
Zizheng Wang
Hanlin Wen
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Feng Yawen Miss
Han Mengdi Miss
Huang Fangming Miss
Li Xiuran Miss
Original Assignee
Feng Yawen Miss
Han Mengdi Miss
Huang Fangming Miss
Li Xiuran Miss
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Priority to AU2019101182A priority Critical patent/AU2019101182A4/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning

Abstract

This invention belongs to the field of credit. It is a classification model for the default risk of credit customer based on deep learning. The invention consists of the following steps: Firstly, we collect real data, fill in missing values, and, therefore, obtain relatively complete and valuable data. Secondly, the preprocessed data set is divided into training set and test set. Thirdly, we use one hybrid unsupervised and supervised method to analyze the training set data. After determining the model, we used grid search to adjust parameters, such as learning rate and depth, to get the optimal performance model. Finally, we use trained model to examine the test set in order to effectively predict the default risk of customers.
AU2019101182A 2019-10-02 2019-10-02 Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning Ceased AU2019101182A4 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2019101182A AU2019101182A4 (en) 2019-10-02 2019-10-02 Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning

Applications Claiming Priority (1)

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AU2019101182A AU2019101182A4 (en) 2019-10-02 2019-10-02 Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning

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AU2019101182A4 true AU2019101182A4 (en) 2020-01-23

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AU2019101182A Ceased AU2019101182A4 (en) 2019-10-02 2019-10-02 Credit Risk Assessment of Lending Borrowers Based on Hybrid Supervised and Unsupervised Learning

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340240A (en) * 2020-03-25 2020-06-26 第四范式（北京）技术有限公司 Method and device for realizing automatic machine learning
CN111723367A (en) * 2020-06-12 2020-09-29 国家电网有限公司 Power monitoring system service scene disposal risk evaluation method and system
CN112381938A (en) * 2020-11-11 2021-02-19 中国地质大学（武汉） Stratum identification method based on trenchless parameter while drilling machine learning
CN112541536A (en) * 2020-12-09 2021-03-23 长沙理工大学 Under-sampling classification integration method, device and storage medium for credit scoring
CN113538079A (en) * 2020-04-17 2021-10-22 北京金山数字娱乐科技有限公司 Recommendation model training method and device, and recommendation method and device
CN113689278A (en) * 2021-06-01 2021-11-23 国网吉林省电力有限公司信息通信公司 Loan client wind control method and system based on electric power big data

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340240A (en) * 2020-03-25 2020-06-26 第四范式（北京）技术有限公司 Method and device for realizing automatic machine learning
CN113538079A (en) * 2020-04-17 2021-10-22 北京金山数字娱乐科技有限公司 Recommendation model training method and device, and recommendation method and device
CN111723367A (en) * 2020-06-12 2020-09-29 国家电网有限公司 Power monitoring system service scene disposal risk evaluation method and system
CN111723367B (en) * 2020-06-12 2023-06-23 国家电网有限公司 Method and system for evaluating service scene treatment risk of power monitoring system
CN112381938A (en) * 2020-11-11 2021-02-19 中国地质大学（武汉） Stratum identification method based on trenchless parameter while drilling machine learning
CN112541536A (en) * 2020-12-09 2021-03-23 长沙理工大学 Under-sampling classification integration method, device and storage medium for credit scoring
CN113689278A (en) * 2021-06-01 2021-11-23 国网吉林省电力有限公司信息通信公司 Loan client wind control method and system based on electric power big data

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