The session will provide guidance for when predictive modeling tools can be effectively used and when they cannot. There will be an opportunity for the audience to ask questions. At the conclusion of the session, attendees will be able to: identify predictive modeling tools that can be used to model extreme events and determine when predictive modeling tools do and do not apply to modeling extreme events.

In this session, we will cover uses of predictive modeling tools and examples of the business impact they can have. Featured topics will include consumer-based and claim-based predictive models, how to assess the reliability of apparent emerging trends, and how to identify situations where a novel predictive modeling technique might generate significant returns. This will take the form of a panel discussion which will leave attendees with a starting point for exploring their own applications.

Understanding the health status of newly insured individuals will become a challenge in 2014. This session will explore the use of health care and non-health care information in identifying and predicting the health status of insured individuals.

What is R? Why should I learn and use R? How can I learn R? Come get answers to these questions and any others you may have about R. Learn about applications in machine learning, statistical graphics, parallel processing, reproducible analytics, incurred but not reported (IBNR) estimation and more. Learn to present data in a way others can understand. Learn about data transparency and data liberation through the presentation of case studies.

2014 will bring about the potential for significant changes in the characteristics of those covered by health insurance. With many new people potentially purchasing insurance, carriers will need to pay attention to the risk of the newly enrolled and develop methods for enrolling these new insureds in their medical management programs. This session will discuss the use of predictive modeling tools to both evaluate the risks insured, as well as to identify those members who would benefit from disease management and other clinical programs.

Actuarial Standard of Practice No. 45 provides guidance on the techniques, applications, procedures and methods that reflect appropriate actuarial practices in the United States in relation to the use of health status for risk adjustment. This is a new standard (January 2012), and thus health practitioners should periodically review this standard's content, intent and obligations. Risk adjustment is becoming a required competency for many health actuaries, particularly in light of expanding application in Medicare, Medicaid, individual and small group marketplaces. This session is intended to meet the professionalism portion of the continuing education credit requirements, but it is up to the attendees to determine whether the content represents relevant continuing education based on the facts and circumstances of their own education needs.

This dissertation by Tia Sawhney will help you understand this issue and start measuring it, or start promoting equitable public policy.

The recent development and availability of sophisticated computer software has facilitated the use of predictive modeling by actuaries and other financial analysts. This article is intended as a tutorial for the analyst interested in using predictive modeling by making the process more transparent. It illustrates the predictive modeling process using State of Wisconsin nursing home cost reports. From NAAJ, July 2007, Volume 11, Number 3.

2010 Seminar - Predictive Modeling for Life Insurance, Tampa. The first part of this session discussed the components, uses and value of the data available from Equifax Marketing Services. The second part discussed data resources for predictive analysis including traditional and non-traditional underwriting data, impact of 3rd party marketing data on life underwriting predictive models, and synthetic variables and data considerations.

2010 Seminar - Predictive Modeling for Life Insurance, Tampa. This session discussed using predictive modeling for life underwriting. Topics included: 1. Historical perspective. 2. Some revealing facts about underwriting. 3. New underwriting techniques. 4. Data supporting life underwriting predictive models.