| Issue |
ITM Web Conf.
Volume 68, 2024
2024 First International Conference on Artificial Intelligence: An Emerging Technology in Management (ICAETM 2024)
|
|
|---|---|---|
| Article Number | 01018 | |
| Number of page(s) | 5 | |
| Section | Engineering Technology & Management | |
| DOI | https://doi.org/10.1051/itmconf/20246801018 | |
| Published online | 12 December 2024 | |
Loan Borrower’s Perception and Awareness on CIBIL Credit Scoring Model
1 Professor, Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India
2 Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India
* e-mail: metilda-mba@dayanandasagar.edu
This study examines the effectiveness of various credit scoring models employed by financial institutions particularly in predicting loan default rates. The growing reliance on these models to decide on lending loans , understanding the factors that influence credit score such as payment history, the length of credit history, the diversity of credit portfolios and the total amount owed has become crucial. This research seeks to assess the awareness levels among borrowers regarding these factors and how their understanding impacts their financial decisions. By employing structured questionnaires and surveys the study gathers data from loan borrowers to understand borrower behaviour and knowledge on credit scores. ANOVA and correlation analysis is used to examine the differences in awareness across demographics variables and CIBIL factors. The findings indicate that demographic factors like gender, age and education levels impact their understanding of the CIBIL credit score factors.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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