The Application Study of Consumer Credit risk model in Auto Financial Institution Based on Logistic Regression

DOI

10.2991/msbda-19.2019.3How to use a DOI?

Keywords

Consumer credit risk, Loan, Logistic regression, Random forest, Auto financial

Abstract

Credit scoring technology is a kind of statistical model, which is widely used for Risk Assessment scoring for loan applicants, which can predict credit risk of applicants, based on information provided by customers, historical data of customers and data from third-party platforms (sesame score, Wechat score, etc.). Based on the data provided by an auto finance institution, this paper completes data processing, feature variable selection, variable WOE coding discretization, logistic regression model development and evaluation, credit scoring card establishment, which provides a reference for the risk control of this auto finance institution.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cuizhu Meng
AU  - Bisong Liu
AU  - Li Zhou
PY  - 2019/08
DA  - 2019/08
TI  - The Application Study of Consumer Credit risk model in Auto Financial Institution Based on Logistic Regression
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 15
EP  - 20
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.3
DO  - 10.2991/msbda-19.2019.3
ID  - Meng2019/08
ER  -