Description: Practical Analytics 4.0 Virtual Event recordings

Description: The CIA, CAS Institute (iCAS) and the Society of Actuaries (SOA) are joining forces once again to launch the Predictive Analytics Hack-A-Thon!

Description: Lincoln Financial Group announced the beginning of a two-year data and analytics talent upskilling program with the Society of Actuaries, a global professional organization for actuaries. The SOA will facilitate training for Lincoln employees through a customized multi-phase educational program focusing on big data and predictive analytics.

Description: The actuarial profession is changing rapidly and the skills needed to be successful are also changing. In this session, we will debate key topics and questions, such as: What is the predominant future career path for actuaries? Specialists or generalists? Does the actuarial profession have a meaningful value prop outside our core domains e.g. insurance? What skills are needed for the actuary of the future? What’s the best approach to improve actuarial storytelling to non-actuarial audiences? What’s the role of actuaries to managing and predicting emerging trends and assumptions? What is the role of actuaries in relation to development and ownership of advanced modeling? Is the future actuarial profession smaller/larger than it is today? In either case, how does it differ from today in terms of make-up, deployment, function, purpose, value add, etc.?

Description: Creating a predictive model is one thing, but then taking that model and getting full stakeholder buy-in is another. Learn from presenters' experiences and insights on proving model effectiveness in the long-term care (LTC), life and annuity spaces. Explore various topics such as feasibility, model transparency, fairness and equity, ROI measurement, data assets and compliance, infrastructure and more. By the end of the session, you will gain an understanding of how to navigate the challenges of proving the effectiveness of predictive modeling and obtaining management buy-in.

Description: Cover a variety of topics centered around the unsupervised learning technique called 'clustering'. The presenters will take you through a wide array of topics that range from 'What is Clustering?' to 'How can I use this technique to solve problems I didn't even know existed?' In the health care industry, everyone is looking for an edge in reducing total spend-cluster analysis finds the unambiguous in an ambiguous framework of results.

Description: Renal disease is a very heterogenous medical condition whose end state is dialysis, a recurring cost intensive medical condition. Dialysis cost is one of the major medical cost drivers in USA. Insufficient and improper care management in the early stages of CKD (chronic kidney disease) leads to early incidence of end stage renal disease (ESRD) among CKD patients which not only leads to high medical costs, but also reflects poor health quality.

Description: If anyone is a paradigm of the notion that learning never stops, it’s Head of Actuarial Modeling and Experience Study for Munich RE Winnie Liu. Winnie set her sights on new horizons after almost 20 years in the insurance industry. Her interest in cutting-edge predictive analytics had been budding for years, and in 2017, she set out to earn her Predictive Analytics (PA) Certificate from the Society of Actuaries (SOA).

Description: View 2018 Kaggle Involvement Program Results.

Description: Explore wellness programs and their effectiveness within the LTC industry by attending the Considerations for LTC Wellness Programs webcast. You will experience discussions about design tactics and evaluation processes for programs, as well as discovering ways to assist policyholders achieve a sustainable and equitable life. There will also be a conversation about the current regulatory environment as it relates to LTC wellness programs. This webinar will focus on subsets of insured populations that are high risk of claim in order to properly manage care decisions and ensure unfair bias is avoided. Learn how to test for unfair bias and determine best possibilities on how to guard against this when testing is unavailable.