Key figures for longevity risk: New models to ensure capital adequacy
The increase in life expectancy influences the risk structure of life insurers. The precise measurement of this development is a key prerequisite for calculating an adequate equity base in order to cover risks and absorb losses. The previous standard models (Lee-Carter) are based on assumptions for the years 1956 to 2020, which no longer match the current data. Empirical data from countries with high life expectancy show a structural change. In Germany, Sweden and the Netherlands, according to mortality tables, a significant slowdown in life expectancy has been observed since 2011. Regional differences in mortality are no longer leveling out; they are here to stay. Quantifying this change using new models is necessary in order to check the adequacy of capital and manage the challenges of longevity risk and the risk margin.
The problem: limitations of stationary models for calculating mortality
Traditional models continue to assume that deviations in mortality return to a stable mean value in the long term. Linear models subsequently force a return to the mean and thus underestimate the actual risk for insurers.
However, this mathematical assumption of stationarity still forms the basis for many internal risk models. Inaccurate modeling leads to a systematic miscalculation of the aforementioned capital adequacy. Precise mapping of longevity risk requires calculation models that go beyond static methods.
The solution: Precise SCR calibration through adaptive model architectures
Evolutionary algorithms and transformer architectures enable the development of adaptive and dynamic solutions. The combination of these technologies enables a more precise calibration of the Solvency Capital Requirement (SCR) as well as compliance with regulatory conformity under Solvency II, etc.
To capture non-linear temporal patterns, the use of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures offers methodological advantages. These models can recognize complex dependencies in the data that conventional methods overlook or cannot be modelled using conventional methods.
The additional use of evolutionary algorithms makes it possible to use the principles of natural selection to find the best mathematical solution to a problem. In actuarial mathematics, they are used for the automated improvement of risk models.
An exemplary implementation can be found within the MDM Booster solution from Open LS with the so-called Sweet Spot Finder. An evolutionary algorithm tests thousands of model variants and selects the most stable approaches for the mortality forecast.
The mathematical selection of model parameters minimizes the risk of human error in the SCR calculation, resulting models are less sensitive to statistical noise and provide more reliable values for capital planning.
Strategic capital management in compliance with regulatory standards
The introduction of the aforementioned AI-based and evolutionary processes requires clear governance. The aim is to prepare the results for the supervisory authorities in a comprehensible manner and to control the drift in long-term forecasts.
With the combination of evolutionary algorithms and the possibility of having individual, transformer-based AI models trained by the specialist department, as well as the traceability functions, the MDM Booster offers an optimal basis for precisely these requirements: Optimized capital management to meet regulatory requirements and complete traceability.
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