Abstract
We consider the issue of modeling the latent or hidden exposure occurring through either incomplete data or an unobserved underlying risk factor. We use the celebrated EM algorithm as a convenient tool in detecting latent (unobserved) risks in finite mixture models of claim severity and in problems where data imputation is needed. We provide examples of applicability of the methodology based on real-life auto injury claim data and compare, when possible, the accuracy of our methods with that of standard techniques. Sample data and an EM algorithm program are included to allow readers to experiment with the EM methodology themselves.
Keywords: EM Algorithm, Claims, Fraud, Missing Data, Mixture Models, Medical Bills.
Volume
Bergen, Norway
Year
2004
Categories
Actuarial Applications and Methodologies
Data Management and Information
Financial and Statistical Methods
Statistical Models and Methods
Publications
ASTIN Colloquium
