A Study of Consumers' Preferences and Perceptions on Life and Medical Insurance
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The document summarizes a study on consumers' (teaching faculty) preferences and perceptions of life and medical insurance. It analyzes demographic data of 105 respondents and finds that qualification has a significant influence on life insurance coverage amounts, while family income influences medical insurance coverage amounts. It concludes that while demographics impact insurance decisions, other economic and social factors are also important. It recommends insurance awareness programs tailored for teaching faculty and making faculty insurance coverage mandatory.
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A Study of Consumers' Preferences and Perceptions on Life and Medical Insurance
- 1. Global Insurance: Practices and Outlook-2017 29th -31st July’17 A Study of Consumers’ Preferences and Perceptions on Life and Medical Insurance Dr. G. D. Pawan Kumar, Faculty, Hyderabad-25
- 2. Objectives 1. To understand the preferences and perceptions of consumers’(Teaching Faculty) regarding Life and Medical Insurance. 2. To assess the demographic variables that impact the Life and Medical Insurance cover. 30/7/2017 2 GDP
- 3. Survey Findings- Demographic details of Sample • Sample Size : 105 • Random selection comprising mainly teaching faculty from Engineering and MBA institutions. • Gender: No’s % Female 25 23.8 Male 80 76.2 • Family Size : 2-5 members 98 93.3 6-10 members 7 6.7 30/7/2017 3 GDP
- 4. Age of the Respondents 26-30 Yrs 5% 31-35 Yrs 16% 36-40 Yrs 20% 41-45 Yrs 19% Above 46 Yrs 40% 30/7/2017 4 GDP
- 5. Educational background of Respondents Diploma 8% Graduation /B.E/ B.Tech 18% P.G/M.E/ MTech 42% PhD 32% 30/7/2017 5 GDP
- 7. Type of Life Insurance preferred Term Policy 26% Endowmen t Policy 26% Whole Life Policy 12% Money Back Policy 32% Other Policy 4% 30/7/2017 7 GDP
- 8. Life Insurance Cover taken Less than Rs 5Lakhs 46% Rs 6- 10Lakhs 30% Rs 11- 15Lakhs 8% Rs 16- 20Lakhs 8% Above Rs 21Lakhs 8% 30/7/2017 8 GDP
- 9. Medical Insurance Cover bought Rs 1.0- 2.0Lakhs 43% Rs 2.1- 4.0Lakhs 24% Rs 4.1- 6.0Lakhs 25% Rs 6.1- 8.0Lakhs 3% Above Rs 8Lakhs 5% 30/7/2017 9 GDP
- 10. Preference for Life Insurance Company NR 10% LIC 63% ICICI Pru 4% Birla Sun Life 4% HDFC Std Life 5% Maxlife 3% Tata AIA 2% Bajaj Allianz 1% SBI Life 6% Others 2% 30/7/2017 10 GDP
- 11. Preference for Medical Insurance Company United India 29% New India Insurance 18% Star Health 13% Max Bupa 4% Apollo 3% Religare 2% Royal Sundaram 2% Others 17% NR 12% 30/7/2017 11 GDP
- 12. Purpose of taking Insurance Cover Short /Long term savings 11% To reduce income tax 22%To financially protect family in case of unforeseen event 38% To reduce financial burden due to hospitalisation 27% Other reason, if any 2% 30/7/2017 12 GDP
- 13. Reasons for Low Cover Premium rates are high 35% I am financially strong and do not require higher value 3% I invest in other asset classes 23% Returns are Poor 16% Other reason if any 23% 30/7/2017 13 GDP
- 14. Objective 2 To find the demographic predictors influencing Life and Medical Insurance cover by Regression Analysis. Demographics considered: 1. Gender 2. Age 3. Qualification 4. Family Size 5. Family Income 30/7/2017 14 GDP
- 15. Findings-Major Influence on buying Life Insurance • Qualification(Q) was found to be significant predictor with p=0.002. • A regression equation was found [F(1,103)=10.239,α=.05)] with an R² of 0.090. • 9% of the variation in life insurance cover was explained by qualification of the customer. • Predicted amount of life insurance is equal to a+b3*X3(Q) which is 0.337+0.424* (Q). • The amount of life insurance increases by Rs 0.42 lakhs for every increase in qualification level. 30/7/2017 15 GDP
- 16. Findings-Major Influence on buying Medical Insurance • Family income(FI) was found to be significant predictor at p=0.005. • A significant regression equation was found with F (1,103)=8.192,p=.005) with R² of 0.074 • 7.4% of the variation in the amount of medical insurance was explained by family income. • The predicted amount of medical insurance is equal to a+b5*X5(FI), which is 1.388+0.222(FI). • Regression showed that amount of medical insurance increases by 0.22 Lakhs for increase in one Lakh of family income. 30/7/2017 16 GDP
- 17. Conclusions 1.It was statistically found that qualification of the customer had significant influence on life insurance cover. 2.There was a reasonable and significant correlation of educational levels with family income(R=.502, p=.000). 3. In case of medical Insurance, family income was seen to have significant influence on buying the cover. 4. It can be inferred that factors other than demographics have strong and positive influence on buying insurance. These could be economic, financial, psychographic or complex social factors but have eluded this study requiring further research into the segment considered. 30/7/2017 17 GDP
- 18. Conclusions in line with… • Zeitz (2003) who observed that “given the changes in technology, demographic factors and the economic environment, many of the significant findings from studies conducted in previous decades may likely be considered obsolete”. • Zeitz , Emily Norman, “ An Examination of the Demand for Life Insurance”, Journal of Risk Management and Insurance Review, (2003) Vol:6, No 2,p 159-191 30/7/2017 18 GDP
- 19. Recommendations 1. An awareness programme should be taken by Insurance companies with policies suitably tailored to target this market- Teaching Community. 2. Ministry of Higher Education Dept. (MHRD) should launch a scheme for the faculty through managements of respective institutions to provide adequate cover under group insurance. 3. Government should make insurance coverage mandatory akin to provident fund by making managements responsible for deducting suitable amount towards life and medical insurance based on the demographics of an employee. 30/7/2017 19 GDP
- 20. Recommendations…..outcome • By providing lifetime cover for the faculty members enrolment could increase substantially bringing the premium rates down, creating a win-win situation for all stakeholders, namely, customers, insurance companies, corporate hospitals and government. 30/7/2017 20 GDP
- 21. May I have your queries, please. 30/7/2017 21 GDP