REMINDER: ASTIN Reading Club & Webinar

We are pleased to invite you to join us upcoming for our upcoming Reading Club and Webinar - Register for both sessions below



Date:  24 October 2023
Time|12:00 PM (CET)

Join the ASTIN Reading Club for a upcoming session on: A representation-learning approach for insurance pricing with images.

Representation learning is at the heart of recent advances in artificial intelligence and can be defined as the ability for predictive models to transform data automatically into the most suitable form for a prediction task. Recent papers applying deep learning within actuarial science have used conventional forms of representation learning, as well as innovated new methods suitable for actuarial purposes.

ASTIN Reading Club

A representation-learning approach for insurance pricing with images.

Speakers: Christopher Blier-Wong
Ronald Richman and Dimitri Semenovich

Read the paper here




Join us in our upcoming webinar on: 
Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims

Date: 06 November 2023
Time: 08:00 AM (EST) |14:00 PM (CET)



Due to the presence of reporting and settlement delay, claim data sets collected by non-life insurance companies are typically incomplete, facing right censored claim count and claim severity observations. Current practice in non-life insurance pricing tackles these right censored data via a two-step procedure. First, best estimates are computed for the number of claims that occurred in past exposure periods and the ultimate claim severities, using the incomplete, historical claim data. Second, pricing actuaries build predictive models to estimate technical, pure premiums for new contracts by treating these best estimates as actual observed outcomes, hereby neglecting their inherent uncertainty. We propose an alternative approach that brings valuable insights for both non-life pricing and reserving. As such, we effectively bridge these two key actuarial tasks that have traditionally been discussed in silos. Hereto, we develop a granular occurrence and development model for non-life claims that tackles reserving and at the same time resolves the inconsistency in traditional pricing techniques between actual observations and imputed best estimates. We illustrate our proposed model on an insurance as well as a reinsurance portfolio. The advantages of our proposed strategy are most compelling in the reinsurance illustration where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes.





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