Credit risk scoring analysis based on machine learning models

Ziyue Qiu, Yuming Li, Pin Ni, Gangmin Li*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

11 Citations (Scopus)

Abstract

In the big data era, institutions can easily access a massive number of data describing different aspects of a user. Therefore, credit scoring models are now building from both the past credit records of the applicant, and other personal information including working years and characteristics of owned properties. A wide variety of usable information has required models to extract more expressive features from data and apply the effective models to fit the features. This paper reports our efforts in using feature engineering techniques and machine learning models for credit scoring modeling. Based on the Kaggle Home Credit Default Risk dataset, several current feature engineering techniques and machine learning models have been tested and compared in terms of the AUC score. The results have shown that the LightGBM model training on expert knowledge generated datasets can achieve the best result (About 78% AUC score).

Original languageEnglish
Title of host publicationProceedings - 2019 6th International Conference on Information Science and Control Engineering, ICISCE 2019
EditorsShaozi Li, Yun Cheng, Ying Dai, Jianwei Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages220-224
Number of pages5
ISBN (Electronic)9781728157122
DOIs
Publication statusPublished - Dec 2019
Event6th International Conference on Information Science and Control Engineering, ICISCE 2019 - Shanghai, China
Duration: 20 Dec 201922 Dec 2019

Publication series

NameProceedings - 2019 6th International Conference on Information Science and Control Engineering, ICISCE 2019

Conference

Conference6th International Conference on Information Science and Control Engineering, ICISCE 2019
Country/TerritoryChina
CityShanghai
Period20/12/1922/12/19

Keywords

  • Credit Scoring
  • Feature Engineering
  • Machine Learning

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