Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: Many life and health insurers are beginning to use Predictive Analytics (PA) for a variety of purposes. This article addresses the unique challenges and potential disruption PA brings to the risk manager, as well as the opportunities.

Description: Highlights of two 2018 health events organized by the Colombian Association of Actuaries and sponsored by the Colombian Government. Seminar on Risk Adjustment and Predictive Modeling in Health. Seminar on Risk Management for the Health Insurers

Description: This article discusses how to handle semisupervised data. This type of data might have a large number of observations, yet a small number of labels.

Description: Collaborative filtering models drive recommendations provided by Netflix and Amazon. This article uses the same models to explore patients’ medical conditions.

Description: Defines Accelerated Underwriting programs as a transformational trend, and outlines a process for evaluating mortality implications of such programs.

Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.

Description: This addresses the changing role for property-casualty actuaries as companies are looking toward actuaries to play a bigger role in moving the business forward.

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk