In the continuously evolving landscape of Artificial Intelligence (AI), Retrieval Augmented Generation (RAG) has emerged as a disruptive technology transforming how we interact with AI systems. RAG combines the strengths of information retrieval and Large Language Models to create powerful applications that can access and use vast amounts of data to generate contextually relevant and […]

Climate Risk scenarios are commonly used in the insurance industry for stress testing, but interpreting and communicating results is often challenging, given strong limitations and complex assumptions. This session will provide practical guidance on stress testing application in the ORSA context, focusing on financial risks, and provide context and foundations necessary in order to communicate […]

Pension funds link generations simply by the time scale of their transactions. Participants pay pension contributions for decades so that getting benefits after retirement, hopefully again for decades. During the course of their career new cohorts start their working life, so there is always active and retired living side-by-side. One group is only paying into […]

Due to the recent shifts in capital markets and a stricter regulatory environment, insurers need to rethink their product offerings to stay ahead of the competition and offer life and savings solutions well suited to the various requirements of policyholders. This includes the need for capital guarantees in combination with strong performance engines and the […]

Life expectancy is a key metric for understanding mortality trends and it varies widely depending on factors such as age, gender and geography. During this websession, we will delve into the latest research and data on mortality trends and explore how they have changed over time. Since the middle of the 20th century, world-wide mortality […]

This web session focuses on the aggregation of individual risks to the overall level of an insurance company. To achieve this, we apply both deterministic and stochastic copula-based approaches, along with key risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). The outcome of this process is the capital requirement, which must be covered […]

In general, machine learning (ML) is the study of algorithms that improve through experience. These algorithms or models can make systematic, repeatable, validated decisions based on historical data. ML has come a long way in recent years, which is reflected in the methods available for time series forecasting (they are also important for assessing parameters […]

In both life and general insurance, many predictive modelling tasks involve outcomes that occur infrequently—such as policy lapses, claims, or fraud. This leads to class imbalance, a situation where the target variable’s classes are not represented equally in the data, often with one class (e.g. policy lapse) being vastly outnumbered by the other. If not […]

For an insurance company, ensuring the proper coordination between assets and liabilities in order to achieve targeted financial objectives is of paramount interest. A strategy used to reach such objectives is "asset and liability management" (ALM in short). ALM can therefore be viewed as any ongoing process that defines, implements, and monitors financial strategies to […]