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Evaluating borrowers' default risk with a spatial probit model reflecting the distance in their relational network
Jong Wook Lee1, So Young Sohn1
1Department of Information and Industrial Engineering, Yonsei University, Seoul, Republic of Korea.
Plos One
|December 31, 2021
Summary
Analyzing loan applicant relationships using spatial methods improves default risk prediction. Incorporating spatial autocorrelation enhances credit scoring models for better financial forecasting.
Area of Science:
- Financial Risk Management
- Econometrics
- Data Science
Background:
- Existing credit scoring models often overlook complex relationships among loan applicants.
- Simple connection information is insufficient for accurate default risk assessment.
- Applicant relationships can significantly impact loan default probabilities.
Purpose of the Study:
- To develop a novel credit scoring approach that incorporates the spatial relationships among loan applicants.
- To quantify the impact of applicant interdependencies on loan default prediction.
- To improve the accuracy of default risk evaluation in lending.
Main Methods:
- Estimating applicant relationships based on characteristic-derived distances.
- Developing and applying a spatial probit model to incorporate relational information.
- Utilizing peer-to-peer Lending Club Loan data for empirical analysis.
Main Results:
- Spatial autocorrelation among loan applicants demonstrates significant predictive power for defaults.
- The proposed spatial probit model effectively captures the influence of borrower relationships.
- Empirical results confirm the value of relational information in credit scoring.
Conclusions:
- Incorporating spatial relationships among loan applicants enhances default prediction accuracy.
- The spatial probit model offers a more sophisticated approach to credit risk assessment.
- This methodology provides valuable insights for financial institutions and peer-to-peer lending platforms.
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