Underwriting Automobile Insurance Using Artificial Neural Networks

Underwriting Automobile Insurance Using Artificial Neural Networks

Fred Kitchens (Ball State University, USA)
DOI: 10.4018/978-1-60566-026-4.ch616
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For hundreds of years, actuaries used pencil and paper to perform their statistical analysis It was a long time before they had the help of a mechanical adding machine. Only recently have they had the benefit of computers. As recently as 1981, computers were not considered important to the process of insurance underwriting. Leading experts in insurance underwriting believed that the judgment factor involved in the underwriting process was too complex for any computer to handle as effectively as a human underwriter (Holtom, 1981). Recent research in the application of technology to the underwriting process has shown that Holtom’s statement may no longer hold true (Gaunt, 1972; Kitchens, 2000; Rose, 1986). The time for computers to take on an important role in the insurance underwriting process may be upon us. The author intends to illustrate the applicability of artificial neural networks to the insurance underwriting process.
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Wireless networking technologies have experienced a considerable development in the last 15 years, not only in the standardization areas, but also in the market deployment of a bunch of devices, services, and applications. Among this plethora of wireless products, wireless sensor networks are exhibiting an incredible boom, being one of the technological areas with greater scientific and industrial development pace (Akyildiz, Sankarasubramaniam, & Cayirci, 2002). The interest and opportunity in working on wireless sensor network technologies is endorsed by (a) technological indicators like the ones published by MIT (Massachusetts Institute of Technology) in 2003 (van der Werff, 2003), where wireless sensor network technology was defined as one of the 10 technologies that will change the world, and (b) economic and market forecasts published by different economic magazines like (Rosenbush, Crockett, & Yang, 2004), where investment in wireless sensor network (WSN) ZigBee technology was estimated over 3.500 nillion dollars during 2007.

Key Terms in this Chapter

Nodal Connections: Connections between nodes in an artificial neural network. They are communication channels that carry numeric data. They simulate the axons and dendrites used to carry electrical impulses between neurons in a biological neural network.

Artificial Neural Network: (commonly referred to as “neural network” or “neural net” ) A computer architecture, implemented in either hardware or software, modeled after biological neural networks. Nodes are connected in a manner suggestive of connections between the biological neurons they represent. The resulting network “learns” through directed trial and error. Most neural networks have some sort of “training” algorithm to adjust the weights of connections between nodes on the basis of patterns found in sample or historical data.

Risk: An individual or organization’s exposure to a chance of loss or damage.

Genetic Algorithm (GA): A class of algorithms commonly used for training neural networks. The process is modeled after the methods by which biological DNA are combined or mutated to breed new individuals. The crossover technique, whereby DNA reproduces itself by joining portions of each parent’s DNA, is used to simulate a form of genetic-like breeding of alternative solutions. Representing the biological chromosomes found in DNA, genetic algorithms use arrays of data, representing various model solutions. Genetic algorithms are useful for multi-dimensional optimization problems in which the chromosome can encode the values for connections found in the artificial neural network.

Node: A mathematical representation of a biological neuron. Multiple layers of nodes are used in artificial neural networks to form models of biological neural networks.

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