id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_pz4j3x2gwfcodfqaa2m6wga6h4	S VIAENE	Auto claim fraud detection using Bayesian learning neural networks	2005	3	.pdf	application/pdf	1494	116	49	Auto claim fraud detection using Bayesian learning neural networks Auto claim fraud detection using Bayesian learning neural networks This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim fraud The automatic relevance determination objective function scheme provides us with a way to determine which inputs are most informative to the trained neural network model. learning is proposed as a practical way of training such networks. The empirical evaluation is based on a data set of closed claims from Keywords: Automobile insurance; Claim fraud; Neural network; Bayesian learning; Evidence framework regarding insurance (claim) fraud prevention, detection and insurance claim fraud. techniques were neural network classifiers trained according importance of inputs to the trained model. Bayesian learning for neural network classifiers is discussed 2. Neural networks for classification	./cache/work_pz4j3x2gwfcodfqaa2m6wga6h4.pdf	./txt/work_pz4j3x2gwfcodfqaa2m6wga6h4.txt