Posted by John Houston on April 21, 2017
As big data has exploded, actuaries lead the way in using predictive modeling and data analysis to uncover insights. In fact, the very first mortality tables were a form of predictive analytics: actuaries used historical data to forecast survival rates and applied that insight to make informed choices about insurance and pensions. Today, thanks to ever more sophisticated algorithms generated by expanding computing power and artificial intelligence, predictive models can even take into account behavioral and economic factors.
In practice, actuarial science is more than crunching numbers; when managing risk, actuaries must also consider the economy, regulatory environment, social science, and strategic positioning of their organizations. Today’s cognitive and self-learning systems automate and augment the math, so that actuaries can focus on gaining an even deeper understanding of their environment and realizing more robust outcomes.
While the actuarial field continues to evolve and take advantage of new technologies, leaders in every industry are now applying the foundational models that actuaries have practiced for centuries. They, too, are using predictive models to make day-to-day decisions with greater wisdom and insight, driving the value of predictive analytics across the enterprise.
Consider these four applications of predictive analytics that come straight from our experience in the actuarial field:
- Propensity for customer engagement or purchase. Actuarial models have long been used to predict the likelihood of an individual’s illness or accident. Now, predictive analytics can be used to predict when a consumer will buy a product or take a trip. Using these insights, companies can precisely target advertising and offer incentives or discounts at the right time. Meanwhile, data science tools can identify relationships and signals that traditional actuarial models would only have found if pointed in the right direction—unexpectedly warm weather triggers sales of hiking gear or a significant anniversary suggests a special celebration.
- Balancing the risk vs. investment ratio. The future doesn’t always work out as planned, so actuaries have learned how to work in an uncertain, changing environment by developing tools to know when and how to account for risk. Many business problems can be solved using these tools. The classic survival model, used to predict life span in the insurance field, can be used to predict when a component will wear out or an inventory will expire. Actuaries rely on traditional asset and liability matching to make sure financial reserves meet potential outlays; this principle easily extends to making sure inventories meet projected demand.
- Improving customer service, planning, and retention. Applying principles from actuarial science, insurance companies focus their limited resources to the right place at the right time. Algorithms help identify the best customers and know when to process a claim quickly, and when to investigate. Likewise, workforce analytics helps focus an organization’s resources to find, attract, and retain the best talent according to desired outcomes and long-term goals.
- Planning for unlikely outcomes. While actuarial models can predict outcomes with reasonable certainty, no outcome is ever completely certain. Much like political pollsters or winning team coaches, actuaries know it’s important to plan for unlikely events that are completely unexpected or unexplained. How do business leaders adjust? A manufacturer knows to identify back-up suppliers if there’s catastrophic weather event; technology providers continuously invest in R&D to stay ahead of the curve. Even in the best possible circumstances, predictive analytics cannot completely eliminate the unexpected but they can help to mitigate its impact.
By applying these lessons, complex data comes to life and delivers valuable insights. How could predictive analytics help you uncover answers to your most challenging business questions and deliver data-driven value?