23 Sep 2021
Micro Reserving in Non-Life Insur.–A Challenge f. Risk Management
Recent surveys carried out among various market players have shown that aggregated reserving methodologies remain preferred in the calculations of non-life insurance reserves. However, the recent development of machine learning in actuarial sciences allows the emergence of new visions of claims and show a growing interest in new ways to estimate reserve based on individual assessment, especially for the atypical and long term risk.
This web session presents a new way to integrate these methods by the actuarial reserving team to produce a complete and innovated process.
As a good data quality is require for this type of studies, the use of data analysis and machine learning could help to check the accuracy of the database and complete it if necessary. Therefore, it allows the company to better understand the impact of each description variables on the claims experience, and thus to define finer homogeneous cohorts and large claims thresholds better adapted to the underlying risk. These preliminary analyses allow to better anticipate future deterioration of specific cases using a segmentation model for large and attritional claims, estimate reserves and prudence margin at a finest level, or challenge the reserving guidelines.
Moreover, the use of individual reserving approach helps to prevent the limitation of aggregated methodologies, especially when the development of claims strongly differs. The use of an innovative micro reserving method like ASICR will help to consider each case’s specificities to estimate the reserves. ASICR provides an Automatic Segmentation of the claims database with the help of data science and projects the future claims development and an Individual Claims Reserving distribution within the helps of actuarial tools.
Reserving can therefore be approached in a different way, with a global and innovative process starting with a precise data analysis and ending with an individual estimate of its reserves. In particular, this enhances the use of data available to the insurer in order to help him define an appropriate risk management policy, in view of the risk profiles that could affect its profitability.
Organised by the EAA – European Actuarial Academy GmbH
The web session is open to all interested persons.
Technical RequirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme Zoom is used for this online training). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature
Thomas LallementThomas is a French certified actuary since 2014 and an ERM/CERA Expert. Actuary graduated from the University of Strasbourg, he joined Addactis France in 2018 as Manager in the Modeling & Risk P&C team and acts as a teacher of non-life reserving for his former school.He is an expert in reserving, Solvency 2 and IFRS 17, and assists all our clients in France and abroad in developing solutions that meet all their needs. Within the Modeling & Risk P&C team, Thomas is also in charge of steering R&D topics on reserving and supports Bryan on his work.
Bryan GautierBryan is a French qualified actuary since end of 2020. Actuary-engineer graduated from EURIA and INSA Rennes, he joined Addactis France in 2020 as a consultant in the Modeling & Risk P&C team.As a specialist in P&C modeling and reserving, he has developed a methodology for individual reserving based on machine learning technics and actuarial projections. Within Addactis, Bryan is regularly involved in P&C reserving issues, both from a french and prudential accounting point of view or IFRS 17. He contributes to the work of the IFRS 17 Implementation Task Force at the international level.