The objective of quantitative credit scoring is to develop accurate models that can distinguish between good and bad applicants (Baesens et al, 2003). Statistical research has focused on delivering new classification methodologies based on neural networks or support vector machines that provide better predictions respect to standard classifiers but poor interpretation. A step forward in such task, it is represented by linear reconstruction of kernel discriminant proposed by Liberati et al (2015) which combines the effective classification results, due to application of no-linear functions, with an easy interpretability of the data. In reality, insolvency, is not always a negative occurrence but the opportunity to generate more profit for a financial institution. Indeed, it can be considered a marketing leverage to reinforce customers’ loyalty. In this paper, we approach credit risk modeling into this perspective. We compare the classification solution that focuses on prediction performance with an explanatory regression as Tobit that models both default probability and duration. Results will be illustrated in a double perspective (risk management and customers' segmentation), in order to identify which covariates affect the insolvent behaviour the most.

Liberati, C., Camillo, F. (2017). Insolvency as opportunity: a marketing perspective on time-dependent credit risk. In Book of Abstracts: 17th Applied Stochastic Models and Data Analysis International Conference with Demographics Workshop ASMDA 2017 (pp.121-121). ISAST: International Society for the Advancement of Science and Technology.

Insolvency as opportunity: a marketing perspective on time-dependent credit risk

LIBERATI, CATERINA;
2017

Abstract

The objective of quantitative credit scoring is to develop accurate models that can distinguish between good and bad applicants (Baesens et al, 2003). Statistical research has focused on delivering new classification methodologies based on neural networks or support vector machines that provide better predictions respect to standard classifiers but poor interpretation. A step forward in such task, it is represented by linear reconstruction of kernel discriminant proposed by Liberati et al (2015) which combines the effective classification results, due to application of no-linear functions, with an easy interpretability of the data. In reality, insolvency, is not always a negative occurrence but the opportunity to generate more profit for a financial institution. Indeed, it can be considered a marketing leverage to reinforce customers’ loyalty. In this paper, we approach credit risk modeling into this perspective. We compare the classification solution that focuses on prediction performance with an explanatory regression as Tobit that models both default probability and duration. Results will be illustrated in a double perspective (risk management and customers' segmentation), in order to identify which covariates affect the insolvent behaviour the most.
No
abstract
Credit Scoring, Tobit Model, Kernel Discriminant, Time-dependent Risk
English
17th Applied Stochastic Models and Data Analysis International Conference
978-618-5180-22-5
Liberati, C., Camillo, F. (2017). Insolvency as opportunity: a marketing perspective on time-dependent credit risk. In Book of Abstracts: 17th Applied Stochastic Models and Data Analysis International Conference with Demographics Workshop ASMDA 2017 (pp.121-121). ISAST: International Society for the Advancement of Science and Technology.
Liberati, C; Camillo, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/157682
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