Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters
AbstractThe main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.
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Bibliographic InfoArticle provided by Savez ekonomista Vojvodine, Novi Sad, Serbia in its journal Panoeconomicus.
Volume (Year): 57 (2010)
Issue (Month): 1 (March)
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Web page: http://www.panoeconomicus.rs/
Natural disaster losses; Insurability; Uncertainty; Multilayer perceptron neural network; Prediction.;
Find related papers by JEL classification:
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
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- J. David Cummins & Neil A. Doherty & Anita Lo, 1999. "Can Insurers Pay for the "Big One"? Measuring the Capacity of an Insurance Market to Respond to Catastrophic Losses," Center for Financial Institutions Working Papers 98-11, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Shiva S. Makki & Agapi Somwaru, 2001. "Farmers' Participation in Crop Insurance Markets: Creating the Right Incentives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 662-667.
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