RT Journal Article
T1 Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies
A1 Segovia Vargas, María Jesús
A1 Camacho Miñano, Juana María Del Mar
A1 Pascual Ezama, David
AB The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.
PB Fundação Escola de Comércio Alvares Penteado
SN 1983-0807
YR 2015
FD 2015
LK https://hdl.handle.net/20.500.14352/34188
UL https://hdl.handle.net/20.500.14352/34188
LA eng
DS Docta Complutense
RD 4 abr 2025