Syed Mehmud




Syed Mehmud, ASA, MAAA, is a Principal and Senior Consulting Actuary in the Denver office of Wakely. Syed is a nationally recognized expert on risk adjustment and actuarial applications of predictive modeling. Through the combination of large scale actuarial projects and developing popular product offerings, Syed has served most health plans in the United States in some capacity. He has worked on a variety of healthcare related projects, particularly involving the application of risk adjustment tools and implementation of risk adjustment methodologies.

Syed has worked on risk adjustment with clients in Medicare, Medicaid, and Commercial settings. His work includes a large-scale actuarial consulting engagement where the Wakely team simulated the HHS risk adjustment methodology in over 30 individual and small group markets across the United States. He has also led the development of several Wakely analytics solutions, such as the Wakely Risk Assessment (WRA) model, Risk Score Opportunities (RSO) analytics, the Wakely RAPID program, the Wakely Risk Insight (WRI) program, and the Wakely Dashboards data visualization platform. 

Most recently, Syed and his team have executed an on-going national-scale project aimed at understanding the drivers of success and challenges in the Affordable Care Act (ACA) program. The Wakely Risk Insight – National Reporting (WRINR) project is a unique lens on the ACA program in that it uses detailed data on millions of ACA lives in order to uncover insights related to succeeding in this program.

Syed co-authored (with Ross Winkelman) the 2007 Society of Actuaries’ study on the comparative assessment of risk assessment models. Syed led a 2012 Society of Actuaries’ study on Uncertainty in Risk Adjustment. His other major published works include Society of Actuaries research project titled ‘Non-Traditional Predictors in Risk Assessment’ (SOA, 2013), and Risk Scoring in Health Insurance – A Primer (SOA, 2016). He is also in the process of writing a book on predictive modeling.