Policyholder retention continues to be challenging for life insurers.
With easy access to pricing online, policyholders can easily check to determine if their policies offer competitive premiums. Policyholders can also easily switch carriers online without meeting with an agent in-person or even making a telephone call.
Another factor that has a direct effect on retention is customer engagement. Life insurers have lower customer engagement when compared to other lines of business such as personal lines home and auto.
Life insurers do not have the ability to easily analyze the policies that are up for renewal. Monitoring policies that are in-force is typically a manual effort that is time-consuming and costly for insurers and agents.
As a result, life insurers are trapped in a reactive mode as opposed to a proactive one.
Part of the reason it has been difficult for life insurers to be more proactive in their approach to review policies is the simple fact that most life insurers have a large volume of data that is spread across multiple policy and claims databases. Many of these core systems are legacy systems that are difficult to access and provide outdated reporting capabilities.
The reporting that most legacy systems offer is often historical statistics that do not enable insurers to develop proactive strategies for policyholder retention. As a result, the lack of ability to know and measure the customer’s experience will impact the renewal decision.
"Aureus Analytics has been a true partner in achieving our retention goals. Their solutions have a direct and meaningful impact on the business and helped us increase revenue by $10M just by improving retention by 1% point."
– Chief Operating Officer, Large National Insurer
With limited resources at hand, life insurers can leverage AI technologies to quickly identify the policies that need to be reviewed across multiple systems without modification to those systems including legacy systems.
The Aureus approach is to implement machine-learning models for policyholder retention that can learn patterns from an insurer's historical data and predict future behavior down to the individual policy level.
Examples of predictive models for policyholder retention that have been developed are:
By implementing these and other predictive models, insurers can develop plans to improve premium collection and develop proactive strategies to reduce customer turnover and increase cross-sell and upsell opportunities with policyholders.
Our approach to helping insurers overcome these challenges is to:
By identifying policyholders that are at risk of non-renewal, and proactively communicating with those policyholders, both retention and revenue can be increased.
Improved Customer Retention
Campaigns can be developed to contact high-risk customers who are identified by the surrender risk model. By proactively contacting these customers, insurers can reduce the number of policyholders who voluntarily surrender their policies.
Increased Premium Collection
Predictive models such as the payment propensity model can identify policyholders who are a high-risk of not paying their policy premium before the due date. Even with a small improvement in premium collection, most insurers will realize a significant increase in revenue.
In addition to improving premium collection, the policy lapse revival model can help increase revenue. This model can help insurers prioritize the policyholders who have the highest probability to revive lapsed policies resulting in additional revenue.
Once you identify policyholders that are at risk of non-renewal, retention can be improved by proactively contacting them.