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March 19  
Persistency Problem- Life Insurance
Published on 11 March 2019
by Ms. Shaily Gupta, Associate Consultant in Business Research


Background and Objective:

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 FY2015, the average 13th month persistency was 61% in FY2016 as reported by IRDA, globally, persistency of these policies is close to 90% in the 13th month and above 65% after 5 years.
India ranks very low in persistency compared to other Asian countries like China, Thailand and Indonesia.  Persistency has been a challenge in India because bulk of the products are saving products.
Objective of Secondary data is to develop 13th month persistency model and to generate actionable insights, while objective of primary data and study consumer buying behavior towards Insurance policies.

Variables under study

Primary Data 

•Product Name
•Buying Medium/Mode
•Annual Premium Range 
•Reasons for investment 
•Awareness about the delay in Premium Payment
•Times payment made in grace period
•Other Variables were Age, Gender, Occupation, Educational Qualification, and Marital Status

Secondary Data

Dependent Variable: Policy lapse 
Independent Variable: 
•Product ID: Life Insurance product ID (confidential) 
•Channel Name- Agency, Bank, Broker, Internet Direct Marketing, Others
•Signature in Vernacular: Is signature done in language other than English?
•Intimation Source: How the company is intimated about policy lapse? Inward, Portal, Telesales
• Zone, Family Size, Age, Annual Premium, New Customer, Underwriting required

Data collection and methodology: 

Primary data is collected based on stratified sampling with sample size of 176 (SEC B1 and SEC B2) and selection of the sample is based on policies and age.
Primary data is collected through online survey and offline method .Cross tabulation and Chi square are source of insights which is used to summarize observations by categories
Secondary data is collected to develop 13th month persistency model and insights are generated on policy lapse. GLM is suited to create persistency model as it can predict binary variable (policy lapse). This characteristic is of great use in the insurance industry since the lapse variable can be either zero or one on a policy level. 
Naïve Bayes uses Bayes’ Theorem, which describes the probability of policy lapse based on prior knowledge of conditions that might be related to Policy lapse.
CHAID decision tree and Random Forest is easy to interpret, as we know what variable and what value of that variable is used to split the data and predict outcome

Insights from Primary Data

Term Life Insurance is mostly bought by age group 25-35 years
Term Life Insurance is mostly bought by age group 25-35 years, since term insurance plans are best when bought at young age. A person buying a term plan in his/her 20s is likely to shell out less .It’s ideal for young professionals as it is cost effective.
Investment is the top most reason to buy life insurance for private sector job respondents, while financial security and tax savings are other popular reasons.
Insurance is indispensable and fundamental to a sound financial plan, since it is an instrument for investment, it would help to achieve long-term goals such as buying a home or planning retirement. Insurance provides diverse investment options that come along with different types of policies. 
 Apart from traditional channel such as agents, respondents have showed increasing interest in buying Term Insurance online.
 
Online term insurance plans are 30 to 50 % cheaper than offline ones. There is no agent involved while buying a term insurance plan online and one can directly connect to the insurance company. This allows to save on the commission that is otherwise paid to an agent
Multiple Policy users invest in Endowment and Money back after Term Insurance 
Instead of buying one policy, many respondents bought multiple plans with different maturities which will help them cope with the changing needs at different life stages. Endowment and Money Back plans are traditional insurance and investment products that are very popular.
Men pay premium in Grace Period 
A grace period is a duration where insurance company offers policyholder to make the payment of premiums. A policy lapses when you skip paying its premium, not just on the due date but even within the grace period—which is typically a month. Is grace period different for male and female?
H0 is no association between premium payment in Grace period and Gender
H1 is there is association between premium payment in Grace Period and Gender
Conclusion: Frequency of Males policy holder paying premium in Grace period is higher than female (Chi square test of association pvalue is 0.018).28% of Male respondents paid premium in grace period.
Survey respondents paying premium in the range of 0-20,000 from age group 25-35 pay late premium charges 
Respondent paying premium of 0-20,000 and from age group 25-35 are mostly millennial they keep experimenting with different investment options. Late payment charges are very low for premium range of 60,000-100,000. Late payment charges for age group 36-45 and 46-55 years are very low.

Insights from Secondary Data

Large family Size and Channels are the important factors in determining the policy lapse
The problem of low persistency is deeply deep-rooted with customer relationship management, and agents’ selling practices (Channels). Compared to bank and broker as channels the lapse rate is 11.4% lower than Internet Direct marketing and other channels which is 12%.Probability of policy lapse is 77% when policy it is brought through agent, since the focus of agents is largely on their upfront commission income
Larger family size leads to lower policy lapse as most individuals, who are simply keen on protecting the family during the most important years.
Age has inverse relationship with policy lapse as age increases chances of policy lapse decreases 
Average age of policy users whose policy lapsed is 35 years. As witnessed in primary data, respondents of age group 36-45 years buy insurance for investment, while age group 25-35 years buy for Tax Saving. 
Underwriting reduces risk and lower the chances of policy lapse
In the insurance industry, the practice of underwriting refers to the process of accepting or rejecting risks. This study signifies that increase in underwriting decreases the probability of policy lapse  Signature in Vernacular language indicates default as it indicates low awareness about policy lapse 
There is 80% probability of policy lapse for family size greater than 1 and age less than 35 years with no underwriting, sold by agents and with portal as intimation source.