Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance

Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance

Konstantina K. Ainatzoglou, Georgios K. Tairidis, Georgios E. Stavroulakis, Constantin K. Zopounidis
DOI: 10.4018/978-1-7998-4805-9.ch014
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Abstract

Credit insurance is of vital importance for the trade sector and almost every related business. Moreover, every policy in credit insurance is tailor-made in order to suit in the best available way the unique needs and demands of the insured business. Thus, pricing of such service can be tricky for an insurance company. In the present chapter, this pricing problem in the field of credit insurance will be addressed through the use of intelligent control mechanisms. More specifically, a way of calculating the price of insurance policies that has to be paid by a prospective client of an insurance company will be suggested. The model will be created and implemented with the use of fuzzy logic, and more specifically, through the implementation of an adaptive neurofuzzy inference system. The training data that will be used for the tuning of the system will be derived from real anonymous insurance policies of the Greek insurance market.
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Fuzzy Inference Systems

The Basic Concepts of Fuzzy Systems

Fuzzy sets were initially introduced by Zadeh (1965) for the representation and management of data that was not in a precise format, but rather fuzzy. The basic idea of fuzzy logic is to provide a specific inference format that allows approximate human reasoning skills to be used in a knowledge-based system. Fuzzy logic can provide a mathematical base in order to capture the uncertainty involved in the human cognitive process, such as reasoning and decision making. The previous approach to knowledge modelling failed to embrace the concept of fuzziness. This was the main reason why techniques such as the first order logic and the classical probability theory cannot deal with the representation and modelling of commonsense knowledge. The necessity of addressing problems of uncertainty and verbal imprecision led to the adoption of the fuzzy logic concepts.

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