R&D Labs ensures that we put innovation on the forefront at Ortec Finance. On the technology side, this includes advising on major architectural decisions, assisting in the development of product increments, focusing on non-functional aspects, and organizing events to share knowledge. On the methodological side, this means we develop or stay up to date with methods that best support our client’s need. In all cases we work closely together with our Chapters and Engineering teams.

Current focus areas


  • High Performance Computing
  • User Experience
  • Distributed Ledger Technologies
  • Machine Learning
  • Cyber Security
  • (Micro) Service Oriented Architectures

Ortec Finance delivers computational intensive simulation models that require a lot of data. There is an increasing need for speed. People desire real-time services while data volumes grow.

At R&D Labs we work and experiment with cutting edge technologies in this field: GPU computing, manycore processors, and distributed programming models that use a serverless or container-based architecture.

As an expert provider of investment decision technology, we design user interfaces for our models that suit the user’s needs. We also focus on making them accessible to a non-expert user. Aside from this, we investigate alternatives to mouse & click interfaces and the potential added value of mixed reality on investment decision making.

We believe that DLTs will have a serious impact on the financial world. At R&D Labs we aim to help to democratize the risk management for robotized financial services and contribute to its global standard. For example, by Prototyping decentralized financial services in the pensions and insurance domain.

The field of machine learning is not widely used and accepted in the field of financial risk management (yet). Regulators often require a high level of economic interpretability on risk management models. Our current research is mainly focused around balancing the interpretability with model accuracy using econometric, statistical and machine learning tools.

Nowadays most of our clients prefer care-free SAAS solutions, that are hosted outside their premises. Applications run on private or hybrid clouds and often need to be connected to client user management systems. Security is key, for both the application software and the used infrastructure.

Markets and clients demand change, while technology makes it easier to combine the best components from multiple vendors into a holistic solution. In order to adapt quickly while maintaining our added value, we define core services that can be used in multiple solutions. Both in UI or API form, as a stand-alone product or as part of a broader solution.


  • Forecasting
  • Investment decision making
  • Climate risk
  • Real estate valuation
  • Option pricing

To support our client’s investment decisions, well founded assessments of future risk and return are essential. We try to scale up academic modelling approaches to real-world applications with more assets and long investment horizons. We develop models that have the right balance between a prediction based on historical data and expert about future risk and return.

Using assessments of future risk and return, we help our clients with their investment decision making. This focus area includes traditional portfolio optimization, robust optimization, dynamic investment policies and robo financial planning.

A changing climate is one of the major long term concerns of institutional investors. We incorporate climate risk in our forward looking forecasting models by constructing economic outlooks for different global warming pathways. Using these outlooks, we support climate aware investment decision making as well as climate risk scenario analysis for disclosure and reporting purposes.

Using advanced econometric models and a unique dataset, we develop models for the valuation of house prices. The prices are explained by characteristics of the dwellings such as location and size. These models can, e.g., be used for tax purposes that require a consistent and explainable valuation.

Especially insurance companies, but also pension funds, can have options embedded in the products they offers. An example would be profit sharing. The valuation of such products requires a risk neutral modelling approach. To obtain projections of future valuations, we focus on combining risk neutral valuation with our real-world forecasting framework.

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