View Proposal

---

Proposer

Karamjeet Singh K.Ranthir Singh

Title

Applying statistical and machine learning techniques to study the joint modelling of insurance claims and lapsation for insureds who subscribed to both automobile and homeowners insurance.

Goal

Description

This project proposes to study the joint modelling of insurance claims and lapsation related to insureds who subscribed to both automobile and homeowners insurance. This is useful to insurers as it may provide valuable insights into the area of rate making hence improving both pricing and underwriting. In the first semester we will look to replicate the study of Guillén et al. (2021). Data is available for policyholders in the Spanish market. In the second semester we shall try to get data from another country (student’s home country, if available) and do a similar study perhaps with some machine learning. We will then make comparisons with the study by Guillén et al. (2021).

Resources

Guillén et al. (2021). Case study data for joint modeling of insurance claims and lapsation. Data in Brief, Volume 39, December 2021. https://www.sciencedirect.com/science/article/pii/S2352340921009148 Frees et al. (2021). Dependence modeling of multivariate longitudinal hybrid insurance data with dropout. Expert Systems with Applications, Volume 185, 15 December 2021. https://www.sciencedirect.com/science/article/pii/S0957417421009581

Background

Url

External Link

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Karamjeet Singh K.Ranthir Singh

Keywords

Degrees

Bachelor of Science in Statistical Data Science