Improvement In Persistency Score for a large Insurer - Persistency improvement using AUPERA to identify the propensity to pay and improving customer contactability for a large insurer.

1. Case Study
Low
Persistency
compared to
industry
standards
Low
Customer
Contactability
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Improvement In Persistency Score for a large Insurer
Aureus Approach
BenefitsProducts
Deployment of AUPERA & C360
Training to Call Center team
Communication by Call Center based on Policy Risk Score
Nearly
2-3%
improvement
in persistency
score
Nearly
2-4%
improvement
in customer
contactability
THIS SEEMINGLY SMALL IMPROVEMENT IN
PERSISTENCY WILL HELP THE CLIENT ADD
NEARLY USD 3.6 MILLION TO THEIR TOP LINE.

2. The Challenge
The client has been in business since 2008-09. Since inception, it has grown from strength to
strength. In recent times, however, persistency rates had become a key area of concern. When
they approached Aureus, the client’s 13th
month persistency rate was less than 50%, which
was far lower than the industry average of 66%.
The persistency problem was further compounded by the fact that, customer contactability
was low due to incorrect or outdated data.
The client had customers falling into two main buckets – monthly and non-monthly premium
paying modes. For renewals, the client customer service team would call up the entire
customer base and remind them to renew their policy, which was either due for renewal, or
in grace period or had already lapsed. This blanket calling in no order of customer likelihood
to pay led to massive inefficiencies for the organization, with little benefit in persistency
improvement.
Using AUPERA, we were able to develop ‘Payment propensity” models that helped The client
bucket their customers in a Red, Amber or Green bucket based on their likelihood to pay.
Persistency Improvements using Predictive Analytics
Of the policies expected to pay in a given month, about 60% came from the lapsed portfolio
and of the rest, nearly half were monthly mode policies. We observed that the payment
behaviorof lapsed policies was quite different compared to that of active policies. Even in the
lapse bucket, there were differences in payment behavior of recently lapsed policies as
against older lapses.

3. Benefits to The Client
In each case, the model buckets the given set of policies into 3 buckets - red, amber and green – according to payment
propensity. Such bucketing allowed the customer service teams to prioritize which customers to call first. Themodelis
run on a monthly basis.
Comparison of model predictions versus actual results generally shows an agreement within 3-4%. Use of predictive
modeling for optimizing calling efforthas helped the client reduce the calling effort by about 50%.
In addition to this bucketing, we also ran household analytics using Customer 360 (C360) to identify customers who
were not reachable. By using a combination of NLP for matching names and addresses and as well as the proprietary
Aureus Network Analysis algorithm, we were able to identify nearly 14000 households with more than one policy.
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Using AUPERA and C360, we were able to improve the persistency for the client by
approximately 2-3% and the contactability improvement was about 2-4%. This seemingly
small improvement in persistency will help the client add nearly USD 3.6 million to their top
line.
About The client
The client is a mid-sized insurance company with about 400,000 customers. With over 7
million lives covered, the client offers solutions for individuals as well as corporate groups.
In addition, monthly mode policies also
displayed a different behavior as compared
to non-monthly mode policies. To account
for these differences, while keeping in sync
with business priorities, we developed 5
different models – to be used for scoring the
following 5 buckets:
Non-monthly policies in the due/grace bucket
Non-monthly policies in the first 2 months of lapse
Non-monthly policies lapsed more than 2 months ago
Monthly policies lapsed more than 2 months ago
Monthly policies in the first 2 months of lapse