Export Publication

The publication can be exported in the following formats: APA (American Psychological Association) reference format, IEEE (Institute of Electrical and Electronics Engineers) reference format, BibTeX and RIS.

Export Reference (APA) 
Alves, B. C. & Dias, J. G. (2015). Survival mixture models in behavioral scoring. Expert Systems with Applications. 42 (8), 3902-3910
Export Reference (IEEE) 
B. C. Alves and J. M. Dias,  "Survival mixture models in behavioral scoring", in Expert Systems with Applications, vol. 42, no. 8, pp. 3902-3910, 2015
Export BibTeX 
@article{alves2015_1765610905421,
	author = "Alves, B. C. and Dias, J. G.",
	title = "Survival mixture models in behavioral scoring",
	journal = "Expert Systems with Applications",
	year = "2015",
	volume = "42",
	number = "8",
	doi = "10.1016/j.eswa.2014.12.036",
	pages = "3902-3910",
	url = "http://www.sciencedirect.com/science/article/pii/S095741741400815X"
}
Export RIS 
TY  - JOUR
TI  - Survival mixture models in behavioral scoring
T2  - Expert Systems with Applications
VL  - 42
IS  - 8
AU  - Alves, B. C.
AU  - Dias, J. G.
PY  - 2015
SP  - 3902-3910
SN  - 0957-4174
DO  - 10.1016/j.eswa.2014.12.036
UR  - http://www.sciencedirect.com/science/article/pii/S095741741400815X
AB  - This paper introduces a general framework of survival mixture models (SMMs) that addresses the unobserved heterogeneity of the credit risk of a financial institution's clients. This new behavioral scoring framework contains the specific cases of aggregate and immune fraction models. This general methodology identifies clusters or groups of clients with different risk patterns. The parameters of the model can be explained by independent variables in a regression setting. The application shows the different risk trajectories of clients. Specifically, the time between the first delayed payment and default was best modeled by a three-segment log-normal mixture distribution and a multinomial logit link function. Each segment contains clients with similar risk profiles. The model predicts the most likely risk segment for each new client.
ER  -