Training

Our next course

Introduction to Predictive Modeling: Techniques and Applications for Insurance Actuaries

How to enrol

Click here to register on the SOA site

Place

Crowne Plaza Chicago O'Hare, Chicago Illinois

Date

November 10–11, 2008

Content

Predictive modeling has entered almost every facet of industry, government, and academia, and its applications for insurance are only beginning to be tapped. This two-day introductory course offers life actuaries a practical, working understanding of predictive modeling tools. Beginning with a discussion of data considerations, the course next provides a review of leading techniques - Neural Networks, General Linear Models, CART, and others.

The remainder is spent on a case study, providing attendees with a solid grasp of how predictive modeling can be applied in the real world of insurance.

Sponsored by: Claim Analytics, the Society of Actuaries, and StatSoft.

Claim Analytics was founded in 2000 by Barry Senensky and Jonathan Polon, both Canadian life insurance actuaries. Barry Senensky and Jonathan Polon had worked for the same major life insurance firm for many years, and knew that data mining techniques and predictive modeling were under-utilized by insurers. They were interested in starting their own business in the life insurance field. Their first predictive model they were commissioned to build was a claim scoring model, that scored ltd [long term disability] [LTD] claims on likelihood of return to work (rtw).

Jonathan Polon had recently led a team of model-builders in creating a highly successful predictive model to detect credit card fraud. He and Barry Senensky were convinced that many more applications for pattern detection and predictive models existed in insurance.

They chose disability claim scoring as their first project for a number of reasons. First, because almost all firms providing group long term disability [LTD] insurance held a considerable database of stored claims information. Finding an adequate amount of data to build a claims scoring model would not be a problem.

A second reason was the costliness of long term disability claims in the field of insurance. Barry and Jonathan knew that, with the scale of investment involved, even relatively minor steps forward in the management of group disability claims management would lead to considerable savings. Claim scoring was a powerful tool in bringing greater efficiencies and reduced costs to disability claims management. Predictive modeling, particularly claims scoring, offered huge potential for software support of claims adjudicators, claims processors, and claims managers.

Claim scoring proved to hold even greater promise than Barry and Jonathan had envisaged. In addition to predicting, with startling accuracy and precision, the likelihood of a disability claimant returning to work, claims scoring became a tool for ongoing improvement of claims management.

Claim Analytics now offers pattern detection and predictive models to major insurance firms to assist in objectivity and accuracy of pricing, reserving, and fraud detection, as well as custom models.

[The proper name of the company is "Claim Analytics," not "Claims Analytics."] [The proper name of the company is "claim analytics," not "claims analytics."]