Industry segmentation and predictor motifs for solvency analysis of the life/health insurance industry.: An article from: Journal of Risk and Insurance

This digital document is an article from Journal of Risk and Insurance, published by American Risk and Insurance Association, Inc. on March 1, 1999. The length of the article is 6113 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

From the supplier: The principal concept of grouping is introduced as a method in solvency studies for the life insurance industry. The main advantages of grouping are enhanced solvency prediction and enhanced interpretation of predictors. Improved prediction is due to industry segmentation or enhanced interpretation from predictor motifs. The models were developed by the method of cascaded logistic regression, which predicts solvency status based on motifs, instead of individual factors.

Citation Details
Title: Industry segmentation and predictor motifs for solvency analysis of the life/health insurance industry.
Author: Etti G. Baranoff
Publication:Journal of Risk and Insurance (Refereed)
Date: March 1, 1999
Publisher: American Risk and Insurance Association, Inc.
Volume: 66 Issue: 1 Page: 99(2)

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