Determinants of Surrenders in Life Insurance: Evidence from Tunisian Periodic Savings
Authors
Certified Actuary (FTUSA) (Tunisia)
Article Information
DOI: 10.51244/IJRSI.2025.1210000269
Subject Category: Insurance and Risk Management
Volume/Issue: 12/10 | Page No: 3104-3119
Publication Timeline
Submitted: 2025-11-02
Accepted: 2025-11-08
Published: 2025-11-18
Abstract
This paper investigates the determinants of policyholder surrenders in Tunisian periodic-savings life insurance using real-world data extracted from a Tunisian insurance company. A logistic regression model identifies the main behavioral and financial drivers of surrender decisions in an emerging North African market context. The analysis shows that prior partial surrender (odds ratio ≈ 6.25), the existence of policy advances (≈ 1.82), and low mathematical reserves (< 80 KDT; ≈ 0.56) are the key explanatory variables. The model achieves an AUC of 0.96–0.97, indicating high predictive accuracy comparable to advanced machine learning approaches reported in developed markets. These findings provide actionable insights for life insurers in emerging economies, supporting targeted retention strategies, improved liquidity planning, and enhanced capital management. The study contributes to the limited empirical literature on surrender behavior in African insurance markets and validates key theoretical predictions from international research in the Tunisian context.
Keywords
Life Insurance, Surrender, Tunisia, Logistic Regression, Actuarial Analysis, Emerging Markets, North Africa
Downloads
References
1. Albizzati, M. O., & Geman, H. (1994). Interest rate risk management and valuation of the surrender option in life insurance policies. Journal of Risk and Insurance, 61(4), 616-637. [Google Scholar] [Crossref]
2. Azzone, M., Barucci, E., Giuffra Moncayo, G., & Marazzina, D. (2022). A machine learning model for lapse prediction in life insurance contracts. Expert Systems with Applications, 191, 116261. [Google Scholar] [Crossref]
3. Bacinello, A. R., Biffis, E., Millossovich, P., Olivieri, A., & Pitacco, E. (2017). Intensity-based frameworks for surrender modeling in life insurance. Insurance: Mathematics and Economics, 78, 212-227. [Google Scholar] [Crossref]
4. Becker, B., Opp, M. M., & Saidi, F. (2022). Regulatory forbearance in the US insurance industry: The effects of eliminating capital requirements. Review of Financial Studies, 35(12), 5438-5482. [Google Scholar] [Crossref]
5. Bobtcheff, C., Casamatta, C., & Villamil, A. P. (2016). Financial intermediation and the regulation of life insurance companies. Journal of Financial Intermediation, 28, 118-132. [Google Scholar] [Crossref]
6. Boonmeekham, A., Phoonsawat, N., & Klongdee, W. (2019). Logistic regression model for lapse analysis of life insurance policy. Burapha Science Journal, 24(2), 730-741. [Google Scholar] [Crossref]
7. Breiman, L. (1984). Classification and Regression Trees. Wadsworth International Group. [Google Scholar] [Crossref]
8. Briys, E., & de Varenne, F. (1997). On the risk of insurance liabilities: Debunking some common pitfalls. Journal of Risk and Insurance, 64(4), 673-694. [Google Scholar] [Crossref]
9. Chang, E. C., & Schmeiser, H. (2021). Valuing the option to surrender in life insurance. Journal of Banking & Finance, 128, 106142. [Google Scholar] [Crossref]
10. Cheng, C., Ding, Y., & Zhou, C. (2023). Surrender contagion in life insurance. European Journal of Operational Research, 304(3), 1234-1248. [Google Scholar] [Crossref]
11. Cox, S. H., Lin, Y., & Pedersen, H. (1992). Mortality tables and lapse rates. Transactions of Society of Actuaries, 44, 489-569. [Google Scholar] [Crossref]
12. Dar, A., & Dodds, C. (1989). Interest rates, the emergency fund hypothesis and saving through endowment policies: Some empirical evidence for the UK. Journal of Risk and Insurance, 56(3), 415-433. [Google Scholar] [Crossref]
13. D'Ortona, N. E., & Staffa, M. S. (2016). The theoretical surrender value in life insurance. Insurance Markets and Companies, 7(1), 31-44. [Google Scholar] [Crossref]
14. EIOPA (European Insurance and Occupational Pensions Authority). (2019). Financial Stability Report. European Union. [Google Scholar] [Crossref]
15. Eling, M., & Kiesenbauer, D. (2014). What policy features determine life insurance lapse? An analysis of the German market. Journal of Risk and Insurance, 81(2), 241-269. [Google Scholar] [Crossref]
16. Eling, M., & Kochanski, M. (2013). Research on lapse in life insurance: What has been done and what needs to be done? Journal of Risk Finance, 14(4), 392-413. [Google Scholar] [Crossref]
17. European Central Bank. (2009). Financial Stability Review. Frankfurt: European Central Bank. [Google Scholar] [Crossref]
18. European Central Bank. (2017). Financial Stability Review, May 2017. Frankfurt: European Central Bank. [Google Scholar] [Crossref]
19. Fang, H., & Kung, E. (2012). Why do life insurance policyholders lapse? The roles of income, health and bequest motive shocks. NBER Working Paper No. 17899, National Bureau of Economic Research. [Google Scholar] [Crossref]
20. Farkas, S., Fringuellotti, F., & Tunaru, R. (2023b). Life insurance surrender risk. Journal of Banking & Finance, 147, 106729. [Google Scholar] [Crossref]
21. Foley-Fisher, N., Heinrich, N., & Verani, S. (2022). How do U.S. life insurers manage liquidity in times of stress? FEDS Notes, Board of Governors of the Federal Reserve System. [Google Scholar] [Crossref]
22. The Geneva Association. (2012). Surrenders in the Life Insurance Industry and their Impact on Liquidity. Geneva: The International Association for the Study of Insurance Economics. [Google Scholar] [Crossref]
23. Ghimire, R., Thapa, B. S., & Waikar, V. (2024). Policyholder surrender behavior in the Nepalese life insurance market during the COVID-19 pandemic. Interdisciplinary Journal of Innovation in Nepalese Academia, 3(2), 86-105. [Google Scholar] [Crossref]
24. Gemmo, I., Götz, M., & Schmeiser, H. (2018). Life insurance and demographic change: An empirical analysis of surrender decisions based on panel data. SSRN Working Paper No. 3230274. [Google Scholar] [Crossref]
25. International Monetary Fund. (2021). Global Financial Stability Report. Washington, DC: IMF. [Google Scholar] [Crossref]
26. International Monetary Fund. (2024). Implementing Risk-Based Solvency for Insurers—Lessons from Kenya, Mexico, and South Africa. Washington, DC: IMF. [Google Scholar] [Crossref]
27. Jansen, M. (2021). Solvency risk and policyholder behavior in the life insurance sector. Journal of Financial Stability, 54, 100876. [Google Scholar] [Crossref]
28. Kgare, M. (2021). Predicting Lapse Rate in Life Insurance Using Machine Learning Algorithms. Master's thesis, University of South Africa. [Google Scholar] [Crossref]
29. Kiesenbauer, D. (2012). Main determinants of lapse in the German life insurance industry. North American Actuarial Journal, 16(1), 52-73. [Google Scholar] [Crossref]
30. Kiermayer, M., Krah, F., Pfeuffer, M., & Weiß, A. (2021). Modeling surrender risk in life insurance: Theoretical and experimental insight. arXiv preprint arXiv:2101.11590. [Google Scholar] [Crossref]
31. Koijen, R. S., Van Nieuwerburgh, S., & Yogo, M. (2022). Why are life insurers exposed to interest rate risk? Review of Financial Studies, 35(12), 5374-5437. [Google Scholar] [Crossref]
32. Kubitza, C., Grochola, N., & Grill, M. (2023). Life insurance convexity. ECB Working Paper No. 2829, European Central Bank. [Google Scholar] [Crossref]
33. Kuo, W., Tsai, C., & Chen, W. K. (2003). An empirical study on the lapse rate: The cointegration approach. Journal of Risk and Insurance, 70(3), 489-508. [Google Scholar] [Crossref]
34. Leiria, M., Dionísio, A., & Ferreira, P. (2021). Non-life insurance cancellation: A systematic quantitative literature review. The Geneva Papers on Risk and Insurance-Issues and Practice, 46(4), 882-914. [Google Scholar] [Crossref]
35. Loisel, S., Piette, P., & Tsai, J. C. H. (2019). Applying economic measures to lapse risk management with machine learning approaches. ASTIN Bulletin, 49(3), 839-871. [Google Scholar] [Crossref]
36. Manteigas, C., Neves, C., & Gonçalves, L. (2024). Understanding and predicting lapses in mortgage life insurance: A data-driven approach. Expert Systems with Applications, 236, 121346. [Google Scholar] [Crossref]
37. National Association of Insurance Commissioners (NAIC). (2021). Liquidity Risk Management Guidance. Kansas City: NAIC. [Google Scholar] [Crossref]
38. Ozdagli, A., & Wang, Z. (2019). Interest rates and insurance company investment behavior. Harvard Business School Working Paper. [Google Scholar] [Crossref]
39. Pitacco, E. (2010). Teaching life insurance mathematics: From the lx's to the risk management approach. ICA 2010 Education/Professionalism Paper. [Google Scholar] [Crossref]
40. Poufinas, T., & Michaelide, G. (2018). Determinants of life insurance policy surrenders. Modern Economy, 9, 1400-1422. [Google Scholar] [Crossref]
41. Russell, D. T., Fier, S. G., Carson, J. M., & Dumm, R. E. (2013). An empirical analysis of life insurance policy surrender activity. Journal of Insurance Issues, 36(1), 35-57. [Google Scholar] [Crossref]
42. Schott, F. H. (1971). Disintermediation through policy loans at life insurance companies. Journal of Finance, 26(3), 719-729. [Google Scholar] [Crossref]
43. Shamsuddin, S. N., Mohmad, S., Hanafi, N. H., Samian, M. H., Juanis, D. J., & Zahir, I. N. (2025). Lapsation logistic regression model: A case in life insurance. International Journal of Research and Innovation in Social Science, 9(7), 4179-4190. [Google Scholar] [Crossref]
44. Swiss Re Institute. (2023). Sigma Report: Global Insurance Review. Zurich: Swiss Re. [Google Scholar] [Crossref]
45. Tsai, C., Kuo, W., & Chen, W. K. (2002). Early surrender and the distribution of policy reserves. Insurance: Mathematics and Economics, 31(3), 429-445. [Google Scholar] [Crossref]
46. Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press. [Google Scholar] [Crossref]
47. Zelinová, S., et al. (2024). The impact of dynamic surrender on guarantees and profitability of life insurance. TEM Journal, 13(3), 2502-2511. [Google Scholar] [Crossref]