Our starting premise is that whilst machine learning techniques are widespread in pricing, they are not being adopted ‘on the ground’ in reserving (certainly in the UK). The idea of the working party is to help move this forward, by identifying what the barriers are, communicating any benefits, and helping develop the research techniques in pragmatic ways.
At the same time we understand the resource and time pressures that reserving actuaries are under and the aim is not to replace existing reserving methods per se, but to start the journey to understanding if and how machine learning may help us in our day to day work.
This is an international working party bringing together experts in this field from around the globe.
Lead: Grainne McGuire
Provide a path to gaining competency in common statistical and machine learning techniques by:
- creating a roadmap of methods to learn
- gather together relevant learning materials and,
- developing notebooks in R and Python with example code.
Lead: Greg Taylor
Bring together and review related papers or work.
Lead: Sarah MacDonnell
Find out where the market is on progressing this:
- what are the barriers?
- what are the benefits?
Lead: Nigel Carpenter
- Identify publicly available datasets that can be used to illustrate reserving techniques.
- Provide a summary of the features available in each dataset.
- Provide notebook examples of how to generate simulated datasets.
Develop and undertake our own research.
We anticipate adding additional workstreams covering issues such as pragmatic considerations, and trust and ethics, as our research develops.
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If you want more information about this research working party please contact the Communities Team.
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