Actuarial Internship – Pricing and Experience Analysis in L&H Reinsurance

With guidance from the team’s actuaries, you will take part in the various stages of the work: understanding insurance benefits and reinsurance mechanisms, preparing data, selecting and analysing assumptions, modelling, checking results and presenting conclusions. Studies may focus on mortality, disability or long-term care, supporting both pricing needs and the development of reserving assumptions. 
The internship is not limited to a single assignment: topics will be defined according to ongoing cases, your progress and your interests. You will therefore have the opportunity to explore several products and methods, working with underwriting and reserving teams and, depending on the projects, medical or legal specialists. Your work will be supervised and reviewed by the team’s actuaries. 

Depending on the projects and your progress, you may contribute to the following assignments: 
 
1. Pricing and profitability analysis 
• Analyse portfolio characteristics, insurance benefits and treaty terms, particularly for creditor insurance and individual or group protection. 
• Help configure pricing tools, prepare portfolio data and incorporate biometric and economic assumptions. 
• Contribute to projecting insurance cash flows and analysing profitability indicators; compare pricing, benefit design and reinsurance structure scenarios. 
• Participate in sensitivity analyses and renewal work. Depending on the cases, explore the pricing of non-proportional covers, including per-life or catastrophe excess of loss, using experience-based or probabilistic approaches. 
 
2. Experience studies and development of biometric assumptions 
• Prepare and validate data provided by partner insurers: consistency checks, analysis of anomalies and completeness, and documentation of data adjustments and their limitations. 
• Analyse risk exposure and claims experience by segment; compare actual results with expected levels and identify trends or deviations. 
• Contribute to developing and validating mortality tables, work incapacity incidence tables or long-term care assumptions tailored to portfolio characteristics. 
• Depending on the topics, use segmentation methods, generalised linear models or survival models to investigate risk factors and assess the robustness of results. 
• Participate in targeted analyses, for example on the specific characteristics of self-employed workers or the effects of regulatory developments, such as the Lemoine law, on creditor insurance claims experience.

3. Tool development and improvement 
• Develop or improve processes in R, Python, Excel or VBA to automate data preparation, checks and analyses. 
• Participate in testing and validating pricing tools and models, ensuring calculation consistency, ease of use and confidentiality of assumptions. 
 
4. Documentation and presentation of findings 
• Draft methodological notes and summaries detailing the data, assumptions, results and limitations of studies. 
• Present work progress and conclusions to the team, prepare the materials required for actuarial review and incorporate reviewers’ feedback.

• A solid grounding in probability, statistics and data analysis; an interest in biometric risks and their applications to life and health insurance. 
• Practical proficiency in R or Python and confidence using Excel; knowledge of VBA. You will receive support in learning to use internal pricing and experience analysis tools. 
• Knowledge of life and health insurance, actuarial mathematics, generalised linear models or survival models would be an advantage and can be developed further during the internship. 
• A rigorous approach to data preparation and checks, critical thinking when assessing assumptions, and the ability to identify the limitations of an analysis. 
• Curiosity about a variety of topics and a willingness to understand the technical and commercial aspects of cases. 
• A strong team spirit and the ability to explain an approach and communicate results clearly, both in writing and orally. 
• Organisational skills, increasing autonomy and respect for the confidentiality of data and methods. 
Previous experience in actuarial work or data analysis would be an advantage but is not essential.