Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters

  • Rim Jemli University of Sfax, Faculty of Economics Sciences and Management, Research Unit in Development Economy, Tunisia
  • Nouri Chtourou University of Sfax, Faculty of Economics Sciences and Management, Research Unit in Development Economy, Tunisia
  • Rochdi Feki University of Sfax, Higher School of Commerce, Tunisia

Abstract

The 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.


Key words: Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.
JEL: G220.

How to Cite
Jemli R., Chtourou N., & Feki R. (2010). Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters. Panoeconomicus, 57(1), 43-60. doi:10.2298/PAN1001043J
Section
Original scientific paper