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Data insights can improve life insurance persistency

Advanced analytics can identify customer life-stages and thereby their insurance needs.

The elements of customer acquisition and customer retention matrices are different in life insurance business, as customer retention for a long period of 7 to 10 years is critical for earning a profit from customer life-time value. Customer retention in life insurance is measured in terms of persistency rate, or the percentage of policies renewed every year over the policy period. In 2015-16, the average persistency rate for life insurance policies in the 13th month was just 61%, according to the Insurance Regulatory and Development Authority of India’s (Irdai) handbook on India’s insurance statistics. More than two-thirds of life insurance policies in the 61st month had lapsed during the year as policyholders did not pay renewal premium.

Globally, the persistency ratio is close to 90% in the 13th month and above 65% after 5 years. The acceptable persistency rate in life insurance is 80% for 3-year-old policies and 60% for 10-year-old policies.

Life insurance persistency in India is acutely low and is clearly hurting life insurance companies. The influencers of persistency rate are all the three stakeholders: life insurers, agents, and customers.

The customer contact points of life insurers are limited and customers tend to lose interest after the initial purpose of tax savings; lacking awareness that the utility of life insurance is over a much longer term. Furthermore, the focus of agents is largely on their upfront commission income. The fundamental cause of the low persistency rate is that individuals still largely perceive life insurance as a tax-saving investment instruments and not as a financial protection tool. A large chunk of life insurance is sold in the last quarter of every financial year. This is the period when tax assessors rush to make investments to reduce tax liabilities. All the three stakeholders suffer losses from the current situation: insurers do not make profits on customers who decide to lapse policies within 6 to 7 years. Customers lose money if they do not persist with their policies long enough, with the entire investment lost if policies are persisted for less than 2 years. The focus of the agents on the high upfront commissions makes them lose out on opportunities of deepening relationships with customers.

Life insurers get new business after spending heavily on marketing and business development and on payment of higher first year commissions. This essentially means the upfront costs of acquiring new customers is very high.

For the insurers, the impact of policy lapses is much wider. The lower the persistency ratio, the higher is the operating expense ratio. The operating expense ratio for most life insurers is in double digits. The key to sustainable profitability of life insurers is in reducing their operating expense ratios to low single digits.

Smart customer handling is vital to improving the customer relationship and to understanding their propensity to lapse, based on their economic profile, risk identification, life-stage and other factors.

We know the factors that typically affect policyholder lapse rates include: product type, distribution channel (or specific individual source of business), the number of recent contacts (with the insurer or agent) socioeconomic characteristics of the customer (such as age and gender), any correlation to policy options or guarantees, the presence of product features (such as policy riders), the policy duration (current and remaining) and the policy term, the customer’s other policies, as well as macroeconomic and tax considerations (in particular the tax deadlines and thresholds).

The problem of low persistency is deeply entrenched and requires addressing product design, customer relationship management, and agents’ selling practices. More needs to be done to convey to consumers the important protection element of policies.

Many efforts have been made to improve persistency. Irdai has been trying to regulate front-loaded commissions and mandate a protection component, while the insurers have constantly been trying to improve their direct connection with customers.

A recent consumer study conducted by LexisNexis Risk Solutions has shown that a large majority of customers (76%) depend on agents to learn about life insurance products before taking a decision. Just over one-third of consumers who participated in the study said they carry out a great amount of research before purchasing life insurance. This suggests a low awareness about life insurance and its purpose, and a need to strengthen the agency relationship and other sales channels.

How can life insurers address low persistency? How can data and analytics help?

Greater digitization of the entire sales process can enhance customer experiences while also allowing effective monitoring of agents and other sales channels. This trend towards a richer customer experience and an assisted self-service model is seen in insurance markets around the world and it is arriving in India too.

There also needs to be greater emphasis on training of employees and agents so they become financial or risk advisers for customers rather than just sellers

Insurers can enhance the understanding past insurance behaviour of customers through a unified view of customer risk profiles, which is possible only if life insurers share data amongst themselves.

There’s an opportunity to use advanced analytics to identify customer life-stages and thereby their insurance needs. This will help in pitching the right products to customers, identifying policy lapsation patterns using predictive modelling and surrogate data like credit scores.

The Indian life insurance industry is increasingly becoming aware of this persistency malaise and efforts are moving towards collaboration, and smart use of technology-driven solutions. However greater efforts are required to bring cooperation and data consistency amongst insurers.

Vijay Kumar is national products manager, LexisNexis Risk Solutions, India.

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