Title
Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
Document Type
Article
Keywords
automobile insurance claim; ReLU; hidden layers; batch size; artificial neural network (ANN); predictive modeling;
Identifier Data
https://doi.org/10.4018/978-1-7998-8455-2.ch009
Publisher
IGI Global
Publication Source
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
Abstract
This chapter presents a thorough descriptive analysis of automobile fatal accident and insurance claims data. Major components of the artificial neural network (ANN) are discussed, and parameters are investigated and carefully selected to ensure an efficient model construction. A prediction model is constructed through ANN as well as generalized linear model (GLM) for model comparison purposes. The authors conclude that ANN performs better than GLM in predicting data for automobile fatalities data but does not outperform for the insurance claims data because automobile fatalities data has a more complex data structure than the insurance claims data.
Comments
Book chapter in Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning.