Accelerating Growth with Data & Advanced Analytics

Access to data and advanced analytic insights accelerates growth and is a differentiator for businesses. The life insurance industry is no different, where unique policy owner and insurance professional needs benefit from streamlined data and analytics access. With data visibility and analytics, you can:

• Act before it’s too late. We know data-sharing frequency is at most typically once a year, but policies can go from in force to lapse pending to lapsed in 31 days.
• Delight clients by being proactive, knowing what they need before they do, and acting in their best interest.
• Differentiate your Business. As competitors adopt better in force servicing practices, those who don’t will lose out on incremental growth, new sales, and key client retention.
• Prioritize Time and Actions. Analytics insights surface the policies and owners that need your focus now inside a large book of business, so you don’t have to wade through.

“And especially from a data perspective, in that realm, there’s a lot of gold in that data. The question is, how do you find it, and how do you make it easy for the people to act on it, to put that in front of them? Things like a policy going into lapse. There’s a number of things in the data that tell you that something substantial has happened in the life of that customer, and gives you the opportunity, then, to really reach out to them, assess their needs, and make sure they have what they need. Because we all know, in the insurance business, nearly half of the U.S. population is either underinsured or uninsured. So, just a huge opportunity for all of us there.” Dave Peters, FLMI, Director of Operations at Principal Financial Group

What does data analytics mean?

Data Science and Analytics is a branch work developed to gather, aggregate, and manipulate data to answer questions at scale. The results of data analytics can sometimes be the deciding factor that influences the direction a business grows. Data analytics are powered by curated and normalized datasets. It requires merging data from many different sources, which needs: a professional and trusted relationship between yourself and each data source; normalizing data to account for inconsistent conventions across data sources; and using data in a way that ensures the privacy and protection of sensitive customer data. With the right dataset, analytics can be used to not only answer what happened in the past, but why? It can help to visualize the current state of a business across key features. Powerfully, it can be used to predict future outcomes of present-day business decisions that helps to empower stakeholders and ensures business resilience. It can be easy to get lost in the weeds with technical data jargon. Great data analytics also tell a great story. The ability to construct intuitive visuals with concrete takeaways is essential to ensure your analytics insights do not go unnoticed because it seems too complicated to understand.

Why Should You Care?

Data analytics and the insights they provide can be the best weapon subject experts have to confidently grow a sustainable business. Data – with all its pros and cons – naturally come with the life insurance business. For every new policy originated, a host of new data points are generated that require regular review and sometimes timely action to ensure the best outcomes for your clients. Accessing all this data is hard, as sifting through all available policy data on a regular basis is overwhelming. This is true especially when you need to analyze and deduce the best course of action to take for every policy. Proformex houses one of the largest cross-carrier, curated, multi-source in force life insurance policy databases in the United States. With it, we build accurate analytical algorithms most relevant to businesses servicing in force policies. Through data analytics we can help critically evaluate large books of business, lessen the burden of manual review processes over hundreds of policies, and let industry professionals focus on clients and what’s in their best interests. “Data truly does drive every part of the experience. And, as a matter of fact, one of our foundational projects was to work on how do we aggregate our data in a way that it makes it easy for us to, I say, push it around as needed. So, when we’re pushing it around, we need to think about the availability of that data. Financial professionals want that data available at their fingertips. If a client calls up and says – what’s my cash value? They expect their financial professional to be able to answer that. So, being able to make sure that we have the data available 24 hours a day, that if there are multiple places where it’s available, that we have consistency across those platforms.” Sheri Rhoades, Vice President of Business Transformation at Penn Mutual

Examples of Data Analytics Applications in Life Insurance

Premium Payment Behavior

Insight into policy owner behavior, especially around premium payments, offers a rich window of opportunity for analytics application. It starts with bringing together critical policy data:

• Policy statements that include modal premium for the policy as a benchmark for comparison
• In force illustrations to capture how premiums paid into the specific policy (or not paid depending on the premium schedule and guarantees) affect the policy cash value over time at current and guaranteed assumptions
• Carrier data feeds that give up to daily insight into actual premiums as they are paid throughout the year, not just at the time of a statement or an ideal scenario outlined in an illustration

With a large enough dataset that includes extensive information on illustrated behavior for specific products and their policy charges and expenses, you can build analytics that predict when a specific policy is likely to lapse given the current values reported on their statements and illustrations. With premium payment data, you can learn in real time how the predicted policy lapse changes as a result of the policy owner’s actual behavior. This provides the opportunity to have a proactive conversation with the policy owner about how their expected coverage and cash value growth is impacted by how the policy is being funded.

Finding a better client-policy fit

Policy owners’ insurance needs change. Analytics applied to the following information can bring focus to owners who may benefit the most from reevaluating their life insurance coverage:

• Core policy details from time of placement or documents such as policy issue date, planned premium, death benefit, insured age, and underwriting class
• Details about changes to life expectancy or mortality charges, such as updates to Commissioners Standard Ordinary Mortality Tables
• Broad visibility into similar in force life insurance products placed since the policy was issued

Data from the time of placement viewed alongside information on how policy mortality charges have shifted since the policy was issued can hint that better policy rates for the same coverage may exist in the market. Analyzing a broad set of current in force product data including the premium, death benefit, and lapse age for insured populations that match your owners’ policies allows you to take the next step of focusing on which products may fit your clients’ needs and how adjusting their current coverage could benefit them. Using data and analytics helps businesses focus. Presented correctly, it also equips agents and advisors with the why that facilitates informed, targeted conversations with their clients.