Understanding of Personalized Customer Credit Risk Based on Selected Attributes

DOI

10.2991/978-94-6463-408-2_28How to use a DOI?

Keywords

credit; risk; management

Abstract

In today’s dynamic business landscape, credit risk assessment has become an essential aspect of financial institutions success, and a comprehensive understanding of the most important variables that affect creditworthiness is critical. This essay focuses on the analysis of customer credit risk based on some updated techniques, specifically using crosstabulation, factor analysis, and logistic regression to investigate the relationship between credit default and personal characteristics. The study uses a sample of credit card customers and considers several relevant variables, including age, housing status and employment status, to determine their impact on credit default risk. The results show that the aforementioned models improve the accuracy and efficiency of credit risk models by identifying patterns and relationships among variables and predicting credit defaults with better accuracy. Additionally, certain personal characteristics, such as age and employment time, can have a significant impact on credit default risk. The study’s findings hold implications for financial institutions, as they can leverage machine learning technology to build more accurate credit scoring models that enable better decision-making. Overall, this analysis provides a valuable perspective on the importance of statistical techniques in improving credit risk assessments, revealing the relationship between customer attributes and credit default risk.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Runqi Jiang
PY  - 2024
DA  - 2024/05/07
TI  - Understanding of Personalized Customer Credit Risk Based on Selected Attributes
BT  - Proceedings of the 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024)
PB  - Atlantis Press
SP  - 242
EP  - 251
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-408-2_28
DO  - 10.2991/978-94-6463-408-2_28
ID  - Jiang2024
ER  -