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Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance

Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance
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Author(s): Konstantina K. Ainatzoglou (School of Production Engineering and Management, Technical University of Crete, Greece), Georgios K. Tairidis (School of Production Engineering and Management, Technical University of Crete, Greece), Georgios E. Stavroulakis (School of Production Engineering and Management, Technical University of Crete, Greece)and Constantin K. Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
Copyright: 2021
Pages: 22
Source title: Machine Learning Applications for Accounting Disclosure and Fraud Detection
Source Author(s)/Editor(s): Stylianos Papadakis (Hellenic Mediterranean University, Greece), Alexandros Garefalakis (Hellenic Mediterranean University, Greece), Christos Lemonakis (Hellenic Mediterranean University, Greece), Christiana Chimonaki (University οf Portsmouth, UK)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
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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