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Modelling dynamic lapse with survival analysis and machine learning in CPI

Author

Listed:
  • Marco Aleandri

    (Università degli Studi di Roma La Sapienza)

  • Alessia Eletti

    (Università degli Studi di Roma La Sapienza)

Abstract

In this paper, we will focus our attention on describing and predicting policyholder behaviour dynamically within the specific context of credit protection insurance (CPI). Banks, in fact, purchase this type of insurance to cover the risk that their borrowers become unable to honor their payments due to death, disability, job loss, critical illness or other causes. Given that a CPI will expire as soon as the borrower prepaid or defaulted, accurate estimates of the related assumptions are necessary to calculate a prudential premium at inception as well as the expected future profitability. The reference data are a proprietary dataset with origination and performance observations on 50,000 individuals who have taken out a loan on the US market. First, we will compare different machine learning models (i.e. logistic regression, accelerated failure time model and random survival forest) fitted on the aforementioned data in a survival analysis setting to predict default and prepayment. In particular, we will find that the random survival forest returns superior estimations regardless of the specific lapse model structure. The other element of the analysis consists of making assumptions on the market dynamics and the underlying actuarial model. The former will allow for the simulation of interest rate scenarios, while the latter will be necessary to calculate CPI profit components such as premium and reserve. The combination of lapse estimation and insurance dynamics will define the CPI profit model which we will use to determine the time value of options and guarantees varying by interest rate features.

Suggested Citation

  • Marco Aleandri & Alessia Eletti, 2021. "Modelling dynamic lapse with survival analysis and machine learning in CPI," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 37-56, June.
  • Handle: RePEc:spr:decfin:v:44:y:2021:i:1:d:10.1007_s10203-020-00285-9
    DOI: 10.1007/s10203-020-00285-9
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    References listed on IDEAS

    as
    1. Outreville, J. Francois, 1990. "Whole-life insurance lapse rates and the emergency fund hypothesis," Insurance: Mathematics and Economics, Elsevier, vol. 9(4), pages 249-255, December.
    2. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    3. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    4. Nolte, Sven & Schneider, Judith C., 2017. "Don’t lapse into temptation: a behavioral explanation for policy surrender," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 12-27.
    5. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 627-627, November.
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    Cited by:

    1. Jorge Luis Andrade & José Luis Valencia, 2022. "A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.

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    More about this item

    Keywords

    Lapse; Default; Prepayment; Credit protection Insurance; Survival analysis; Machine learning; Accelerated failure time model; Random survival forest; TVOG;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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