My colleague at Pinnacle Actuarial Resources, Inc., Gary Wang, often says that he’s lucky. As a consulting actuary in the field of predictive analytics, Gary lives the exact life he envisioned for himself when he was a kid. I understand how Gary feels. We are fortunate to work in an exciting and expanding industry absolutely filled with opportunity. We get to work with great clients and tackle interesting projects on a daily basis. It’s an exceptional time to be in predictive analytics and I agree with Gary: We are incredibly lucky.

The worlds of gambling and insurance are similar in many ways. Both are built upon elements of probability, modeling and quantification of risk. Both use a variety of means to attract individuals to participate. Professional gamblers are well informed as to the odds of one play versus another; an understanding of modelling can improve those odds to improve the chances of winning. Professional underwriters and actuaries understand premiums must be adequate to pay future claims and expenses; an understanding of modelling helps quantify risk and improve the ratemaking process.

It is certainly nothing new that data privacy and questions over how different organizations collect, maintain, and utilize an individual’s data is a hot topic among consumers, lawmakers, and companies alike. Past data breaches from several well-known companies have raised the antennae on the topic, and ongoing questions around what data is being stored and how it is being used by other high-profile corporations continue to draw attention.

The on-demand culture has been rapidly growing over the past decade. Groceries, car rides and dinners can all be purchased and promptly delivered with the touch of a button and insurance is catching up. At the 2019 Pinnacle U event, we presented on the topic of on-demand insurance and what its future may hold. We partnered with Noah Sierer, an Illinois State University student, to provide examples of current insurers and the market’s potential.

When it comes to advanced analytics such as building predictive models, many people immediately think about the vast amounts of data, computing horsepower needed, and very sophisticated (often mysterious) techniques applied to the data to produce the results. Without question, all of these aspects are important steps in the process. However, there are several other critical steps both before and after these that can truly make or break an advanced analytics project. We can think of this as the Modeling Lifecycle.