Importance of understanding lapse pending policy trends
In an industry where access to current information is a challenge, taking timely, data-driven action on a client’s behalf is a powerful business differentiator. A policyowner falling into grace period on their policy is one of the most critical moments for engagement to protect policyowners and their beneficiaries. Bringing these two together, understanding how policies in a book of business are lapsing and having insight into what that means for a policyowner can drive some of the most meaningful interactions between an advisor and their clients.
Using a deidentified, aggregated dataset of over 800,000 policies across 30 carriers generated from the Proformex network of data, we can identify trends and insights for our customers around policies entering a lapse pending period.
Real-world lapse pending observations on a broad network of data
Understanding the dynamics of lapse pending policies in our network of data is an important component of alerting our customers to policies that may need attention at the right time. For the insights outlined in this whitepaper, we wanted to investigate policies with current, frequent information. This included policies that:
- Were in force as of May 2021
- Had at least a monthly frequency of data feed updates from the carrier for the last year
- Came from carrier feeds with reliable policy status information
The policies analyzed span life product types including term, whole life, universal life, variable, and indexed products. The dataset included policies for a range of insured demographics and policy issue dates.
Critical information with time left to act
What did we learn?
One percent of all in force policies transitioned to lapse pending each month. That is nearly 10,000 policies in our network that need intervention monthly and there is time left to act.
Of those lapse pending policies in a given month, 12 – 15 percent flip between in force and lapse pending for months at a time, often over more than a 6-month period. This finding was true across all product types.
We wanted to understand how long each policy stayed in lapse pending status once it transitioned out of in force. We looked specifically at policies where we were getting daily data updates, which is the most frequent option for any individual policy via carrier data.
We found that the majority of lapse pending policies only stayed in that status for roughly a week, with another group staying in lapse pending for around 30 days. What scenarios might lead to the short duration of lapse pending? One possibility could be the amount of time between when a premium payment is sent by the policyowner and the time it is applied to the policy. The longer, 30-day durations fit the typical expectation for a policy that goes into grace and eventually lapses.
Armed with this information, an agent or advisor can have a tailored, insightful conversation with their client. They know that if the policy is lapse pending but just recently changed status and the owner has a history of near due date payments, they could have a quick check in to verify a premium payment went out. If the policy had been in the grace period for longer than a week, they would likely need a more immediate conversation about the risk of the policy lapsing.
Term policy lapse observations
Attention to term policies in grace period can require different engagement with policyowners than with permanent products. We looked at a snapshot of term policies that went into grace period in May 2022. A majority of those policies were in grace with time left before the conversion date.
After noticing a few peaks in years until conversion end date, we separated term policies into 10-, 20-, and 30-year term duration groups and a trend emerged. In each of the groups, there was a spike in term policies going to lapse pending status with 5 years left until the term conversion date. For the 20- and 30-year term groups, there was a second group of policies that went into grace period concentrated around 15 years to the conversion date.
This information can help agents and advisors plan critical conversations about their clients’ coverage at the time when trends in data show it is most important. Term policyowners who let their policies lapse with time left before the conversion period ends could miss out on converting to a permanent product that is better suited for their needs or miss an opportunity to create liquidity by life settling the policy. A well-timed conversation about a client’s term coverage with 5 years left to the conversion date could have a higher impact for the policyowner than the same conversation at a different stage in the policy duration.
Using this information
Access to current information about life insurance policies and lapse pending trends can help insurance professionals take critical action at the right moments. We have only presented a few of the insights that can be generated from applying analytics to current lapse pending policy trends in the life market. Information such as this, when considered alongside a specific book of business, can help agents and advisors proactively prepare the right conversation for their clients’ best interests.
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About Amanda Yoho
Dr. Amanda Yoho serves as the Chief Technology Officer at Proformex, where she has focused on building out proprietary analytics around life insurance policy lapse risk, architecting a scalable data platform focused on in-force policy management, and growing a cohesive team of curious, results-oriented product managers and engineers to build and expand our scalable data platform and web application that provides customers unprecedented visibility into their business. Prior to joining Proformex in January 2020, Amanda was the Director of Research Informatics with IBM Watson Health, where she supported the business in curating, enhancing, and gaining insights from the largest clinical dataset available for commercial research. She earned her Ph.D. in theoretical physics from Case Western Reserve University (CWRU), where her research centered on detecting anomalies in cosmological data through simulation of the earliest moments of the Universe.
