One of the issues that many insurance companies face in their day to day operations is policy lapse. This happens when an insured misses their premium payment, meaning the insured is no longer covered by their insurance policy and they will no longer enjoy the benefits that come with it.
If a person loses their active life insurance coverage, for example, then their beneficiaries can no longer expect to receive insurance money from the policy in case the policy holder passes away.
Missing a premium payment does not necessarily mean that an insurance policy has lapsed; more often than not, insurance carriers give their clients a grace period of around 30 days—or even 60 to 90 days in special circumstances, such as the current pandemic—to settle their dues.
If the insured is unable to make a late payment within this buffer period, then the policy is considered to have lapsed.
Preventing Policy Lapses: A Priority for Insurance Carriers
There are plenty of issues and missteps that can prevent consumers from making their insurance payments on time.
While missed payments can seem like a minor setback to consumers, insurance carriers that are tasked to deal with a deluge of delayed insurance payments and policy lapses may find themselves in deep trouble, as their overall profitability will fall.
This is especially true now when there’s a striking imbalance between insurance premium payments and payouts due to the current health crisis. It’s imperative, then, for insurance companies to control policy lapses.
In the digital age, this can be done with the assistance of a life insurance innovation that makes full use of a strong data architecture.
How Can Strong Data Prevent Policy Lapses?
Finding out the reason for the policy lapses is the first step in preventing it. This can be done by carrying out a time series analysis using a data model that’s customized to the insurance provider.
Among the factors that should be considered when designing the said data model are:
- Granularity. Which segments of the market are involved in policy lapses? These may be individual policyholders or institutions.
- Period. When did these policy lapses take place? The time stamps are integral in analyzing the data and distinguishing one data set from another.
- Location. Which geographical area did the data set come from? This can also consider whether the party involved is staying in their permanent residence or workplace or not.
- Data Status. The status of the cases as they’re being analyzed also plays a key role in determining the attributes that policy lapses share.
A strong data collection and management system plays a central role in making the aforementioned information available to analysts and researchers. Once the pertinent data from different sources have been gathered in one place, the analysts can get to work.
They can start clustering similar attributes and study them further, and they can also use machine learning to help them predict the characteristics that make it more likely for an insurance holder to miss their payments.
Using the information they’ve acquired so far, data analysts can incorporate enhancements to their process.
They can see if the policy lapses are related in any way to particular business segments, such as group insurance, life and annuity, and health or specific stages in the life cycle of an insurance product such as underwriting, quotes, and claims.
From here on, they can point their finger at exactly what causes a policy holder to allow lapses.
Analyzing data at a granular level empowers insurance companies to identify and keep track of market segments that are at higher risk of letting their policies lapse. They would also be able to formulate timely responses for when a significant portion of their market shows signs of financial instability.
Once the rate of policy lapses has been controlled, the insurance company can start working on prevention measures that will keep these incidences within acceptable numbers.
From the perspective of a policy holder, it’s quite easy to miss premium payments. Such an event can take place when the insured no longer has the capacity to pay for their premiums, but it can also be due to clerical errors and small mistakes.
For example, the insured might have moved to a different address and had yet to update their bank or insurance provider. If they still rely on physical mail to receive premium payment notices, then they likely won’t receive their notice for the month.
The burden of anticipating these minor issues, then, falls on insurance providers and the companies that depend on premium payments to meet their responsibilities.
With the assistance of digital technology and data, reducing policy lapse and providing customers with exceptional experiences will be much easier for insurance companies.
If carried out successfully, using data to prevent policy lapses will prove to be a win-win situation for both the insured and their insurance carrier.