TY - GEN
T1 - Insurance market risk modeling with hierarchical Fuzzy Rule Based Systems
AU - Alcalá, R.
AU - Cordón, O.
AU - Herrera, F.
AU - Zwir, I.
PY - 2002/1/1
Y1 - 2002/1/1
N2 - The continued development of large, sophisticated, repositories of knowledge and information has facilitated the accessibility to vast amounts of data about complex objects and their behavior. However, in spite of the recent renewed interest in knowledge-discovery techniques (or data mining), the usefulness of these databases is partially limited by the inability to understand the system-related characteristics of the data. Some applications from the financial or insurance market-such the ones concerned with risk analysis-require to meet solutions that emphasize precision while aiding to understand and validate their structure and relations. We present results about an ongoing project being carried out by the Argentinian State Insurance Agency for tracking the status of the insurance companies, i.e., for screening and analyzing their condition through time. Specifically in this paper, we will tackle with the modeling of the mathematical reserves of the premiums, or risk reserves, of the insurance companies in the local insurance market. To do so, we propose the use of Linguistic Modeling which is one of the most important applications of Fuzzy Rule-Based Systems. Particularly, we apply Hierarchical Linguistic Modeling with the aim of obtaining the desired trade-off between accuracy and interpretability of the system modeled, i.e., decomposing such nonlinear systems into a number of simpler linguistically interpretable subproblems. The achieved results will be also compared with global hierarchical methods and other system modeling techniques, such as classical regressions and neural networks.
AB - The continued development of large, sophisticated, repositories of knowledge and information has facilitated the accessibility to vast amounts of data about complex objects and their behavior. However, in spite of the recent renewed interest in knowledge-discovery techniques (or data mining), the usefulness of these databases is partially limited by the inability to understand the system-related characteristics of the data. Some applications from the financial or insurance market-such the ones concerned with risk analysis-require to meet solutions that emphasize precision while aiding to understand and validate their structure and relations. We present results about an ongoing project being carried out by the Argentinian State Insurance Agency for tracking the status of the insurance companies, i.e., for screening and analyzing their condition through time. Specifically in this paper, we will tackle with the modeling of the mathematical reserves of the premiums, or risk reserves, of the insurance companies in the local insurance market. To do so, we propose the use of Linguistic Modeling which is one of the most important applications of Fuzzy Rule-Based Systems. Particularly, we apply Hierarchical Linguistic Modeling with the aim of obtaining the desired trade-off between accuracy and interpretability of the system modeled, i.e., decomposing such nonlinear systems into a number of simpler linguistically interpretable subproblems. The achieved results will be also compared with global hierarchical methods and other system modeling techniques, such as classical regressions and neural networks.
KW - Fuzzy Rule-Based Systems
KW - Genetic algorithms
KW - Hierarchical knowledge base
KW - Hierarchical linguistic partitions
KW - Insurance market
KW - Linguistic Modeling
UR - http://www.scopus.com/inward/record.url?scp=84909588696&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84909588696
T3 - ICEIS 2002 - Proceedings of the 4th International Conference on Enterprise Information Systems
SP - 325
EP - 333
BT - ICEIS 2002 - Proceedings of the 4th International Conference on Enterprise Information Systems
A2 - Filipe, Joaquim
A2 - Piattini, Mario
A2 - Braz, Jose
PB - ICEIS Press
T2 - 4th International Conference on Enterprise Information Systems, ICEIS 2002
Y2 - 3 April 2002 through 6 April 2002
ER -