Description: This session will explore the national landscape of state Medicaid risk adjustment program design & application and the key differences that drive effective managed care organization program management. Risk adjustment is a mechanism to adjust capitation rates between Managed Care Organizations to account for population morbidity differences. Although many states utilize this mechanism, the application of risk adjustment can vary significantly across states. The primary differences include populations, risk score models and data collection. These state application nuances affect the strategies and tactics utilized by MCOs for effective risk adjustment program management including provider financial alignment, encounter data submission and the capture of accurate diagnosis codes. The session is intended for Medicaid Managed Care Actuaries working to develop or support comprehensive risk adjustment program management functions.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: In Medicaid rate development, actuaries have not historically shared models that developed the margin (i.e., underwriting gain) assumption. This is unfortunate, given the margin assumption is one of the most critical components of the rate development in ensuring financial solvency of the Medicaid managed care organizations (MCOs). In this session, we will discuss the primary components of the this assumption and will share working models that help actuaries quantify these components—including the cost of capital and risk (or contingency) margin. We expect a lively discussion as we “open the black box” and discuss these actuarial assumptions in the public for the first time.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: The interplay between technology and design to create safe, comfortable and thriving environments for aging in place and managing care costs.

Description: This report pulls together various findings on long-term care research from the 2017 Risks and Process in Retirement Survey as well as other SOA research.

Description: This report is the result of the Long-Term Care Medical Symposium and aims to provide the long-term care actuary with environmental context for considering morbidity and other macro factors that may impact LTC experience in the long term.

Description: View report on Long Term Care Experience Basic Table Development.

Description: Why Should Life Insurers Care About Brexit?

Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry