Fraunhofer Institute for Industrial Mathematics ITWM
Fraunhofer Institute for Industrial Mathematics ITWM
Rente Symbolbild
© iStockphoto
Rente Symbolbild

© iStockphoto

Retirement Provision and Life Insurance

In the area of »Retirement Provision and Life Insurance« we have a holistic view of old-age provision in Germany and Europe in close cooperation with the Produktinformationsstelle Altersvorsorge gGmbH (PIA). For example, we use our technology for the stochastic simulation of old-age provision products for the opportunity-risk classification of tariffs from the customer's perspective.

Example Projects und Information on the Projects

 

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Solvency II Key Figures – Prediction and Explainability With Artificial Intelligence

Our team develops mathematical models and AI methods to calculate the solvency capital of insurance companies. These support the risk assessment and capital requirements under Solvency II in order to efficiently meet financial stability and regulatory requirements.

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Strategic Asset Allocation (SAA) and Portfolio Optimization

Asset allocation refers to the distribution of assets across different asset classes. The framework is provided by the EU Solvency II guideline. On this basis, we have developed and implemented an innovative new approach to strategic asset allocation with R+V Lebensversicherung AG.

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Interview with Dr. Normann Pankratz, Debeka Insurances

Standardized Evaluation of Pension Products

The ITWM team is now working with Debeka in the project »Transparency Initiative for Occupational Pension Plans« for more transparency. In this interview, Dr. Normann Pankratz, member of the executive board of Debeka Insurances, explains what this means and what the cooperation with Fraunhofer-ITWM looks like.

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Researchers in Financial Mathematics Calculate Smart Solvency Capital

Insurance companies must regularly present the so-called solvency ratio to the public. This is intended to provide indications of how crisis-proof the providers are. The calculation is very complex and specific, and many companies only perform it once a year. Financial mathematicians are helping to calculate the solvency ratio using artificial intelligence (AI). What this means is explained in an interview with Dr. Stefan Mai:

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Classification of Private Retirement Provision

Since 2016, we have been classifying all retirement provision products that are subsidized by state allowances on behalf of the Product Information Office for Retirement Provision gGmbH (PIA). These products require a product information sheet in accordance with the Retirement Improvement Act (Altersvorsorge-Verbesserungsgesetz), which in particular identifies an opportunity/risk class (CRK). The Federal Ministry of Finance (BMF) has assigned the task of defining this CRK to PIA.

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ALMSIM®: Asset Liability Management

The object of the Asset Liability Management (ALM) is the financial optimization of the balance positions with respect to the return, risk requirements, liquidity obligation, etc. Therefore, it represents an essential part of the corporate management, especially for banks and insurance companies. It gains more and more importance due to the increasing requirements concerning risk management.

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ALMSIM®-Pfadgenerator: Simulation of Market Scenarios and Contracts

Making the increased market of pension products more transparent is the aim of different national and international initiatives. They have in common to classify the contracts on the basis of key figures derived from the products’ risk profile. Simulations of standardized contracts are used to generate the risk profiles.

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Valuation of Derivatives and Portfolio Optimization

The main focus of portfolio optimization is the determination of an optimal investment strategy at the financial market. In fact, the investor needs to decide how many proportions of which security paper to hold in order to maximize the benefit out of the final assets in the investment horizon.

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Risk Management

Risk management and risk reporting are constantly challenging financial institutions. Thereby the tasks range from the calculation of risk indicators (e.g. Value-at-Risk (VaR)) and the execution of stress tests to some backtesting. We offer you support in the implementation of all occurring tasks, from the generation of includable modules to a complete solution.

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Suitable Topics of Our Doctoral Students and Post-Docs

Mathematics and Machine Learning: Solvency Capital Requirements

It researches Mark-Oliver Wolf.

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Durability: Stochastic and ML-based modeling

It researches Simon Schnürch.

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Prediction of the yield curve

It researches Franziska Diez.

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