Analytics is a growing area in all industries and we’ve seen a particular emphasis on data analytics and modeling in the insurance world. Strong analytical analysis – in particular, predictive modeling – leads to more precise claims determination as well as pricing sophistication. Furthermore, better data analysis saves companies both time and cost and provides for more overall consistency. Thus, we’ve seen a trend in companies focusing their hiring on skill sets dedicated to predictive modeling to solve problems within specific lines of business in the insurance industry.
On the Property & Casualty side, there has been a strong push, particularly in the past five years, to implement predictive modeling departments within insurance companies. Of particular note is the growth of predictive analytics in commercial lines and the continued focus and growth of such areas on the personal lines side. We’ve seen job growth for actuaries and modelers at companies which may not have utilized predictive modeling methods in the past. This gives increased credence to predictive modeling in multiple areas such as pricing, claims and underwriting while dedicating increased human capital resources to these areas. This also is demonstrated in the increase in roles on the consulting side.
Insurers and reinsurers are turning to consulting firms as a resource to bring more of a predictive modeling focus to their analysis as insurance companies also work to improve their internal analytics strength. Implementing sophisticated data analytics allows companies to understand the markets in which they work and to find patterns of behavior and outcome. The use of analytics also allows for more complexity in pricing methodologies, which can set companies apart from their competitors. In order to do this effectively, companies need clean and complete data to work with; companies also need people who know how to convert data into a map of consumer behavior.
Additionally, the more the data and analytics grow, the more they can both be built into the infrastructure and become a real-time tool for insurers to use to assess the risk profiles of consumers… and evaluate and determine pricing. As the world becomes increasingly technologically driven and greater and deeper data points are available, predictive modeling truly is becoming a more sophisticated art.