Enterprise Risk Management Technology Guide 2022 :: Ortec Finance: Solutions to navigate a changing climate

Ortec Finance: Solutions to navigate a changing climate

Are climate and ESG risks still the top concerns for insurers in today’s volatile environment?

Hens SteehouwerAbsolutely! When we speak to our clients they are among their top-of-mind topics. The consequences of the war in Ukraine and high inflation receive lot of attention currently from insurers as well. But these issues of security, energy-transition and food are interlinked with the climate and sustainability challenge.

Ortec Finance has a suite of climate-related solutions to support insurers in this area. For example, our leading climate scenario solution, Climate MAPS, quantifies investment portfolio exposures to transition, physical and market-related climate risks.

Based on a collaboration with our partner Cambridge Econometrics, our scenarios can incorporate climate risks and opportunities associated with different global warming pathways. Our Climate ALIGN solution monitors portfolio alignment with net-zero emissions by 2050.

Based on our brand-new partnership with ESG Book, formerly Arabesque S-Ray, we will later this year launch an on-demand Implied Temperature Rise (ITR) analytics platform for our alignment solution, which will be powered by ESG Book’s market-leading climate data. The platform will allow insurers to access ITR scores across multiple asset classes including public equities, credit, and private markets.

Can Ortec Finance help insurers with inflation risk?

Based on our comprehensive ALM modelling platform, and the unique frequency domain approach of our Economic Scenario Generator, we can help insurers with an integrated, consistent and accurate assessment of the potential short- and long-term impacts of inflation risk on assets and liabilities, for different product lines, entities, as well as at group level.

These same tools can optimise the risk and return profile of the investment portfolio against insurers’ return objectives and solvency risk limits in an efficient and effective way. This can be used to make a portfolio more resilient to inflation risk by rotating more into inflation hedging assets.

How do you expect risk modelling to change over the next five years?

Besides the further integration of climate and ESG, I expect to see an increasing importance and growing adaptation by insurers of what can be called “dynamic ALM”, compared to traditional more short-term static interpretations of ALM. Traditionally, a lot of effort goes into assessing the current balance sheet and corresponding short-term capital requirements. These are very important of course. But another question is how to get insights on the potential future developments of the balance sheet under a wide range of scenarios.

Several insurance companies around the world are now picking up on the benefits of this approach. The required technology available is modular, flexible for integration into insurance technology infrastructures, and we have the experience of implementing it with clients. However, it does not yet have the widespread use in insurance, as it does in the pensions industry.

The driving force behind this change is that insurers must continue to provide decent returns to stakeholders in a world of continued consolidation and increasing competition, despite the recent increases in interest rates, and low yielding insurance investment portfolios.

I also expect to see an increasing application of artificial intelligence (AI) techniques to support risk management and investment decision making. A first driver of this change is of course the increasing volume of available data. Another, less well-known, driver is the continued advances in combining the dynamic ALM models powered by stochastic scenario engines, with that of AI technology to optimise dynamic investment strategies.

How are your insurance clients’ stress and scenario tests evolving?

In an increasingly uncertain world, we see a growing need and application of scenario-based stress testing and sensitivity analysis by insurers in their risk models. Constructing deterministic scenarios, or alternative expectations for stochastic models can be quite challenging nowadays. 

For example, the dimensions in terms of risk drivers and time-periods can be large. In times of stress, fast ad hoc scenario development is required, as was the case after the outbreak of the pandemic. And, of course, the scenarios should come with intuitive narratives that help communications with management and regulators.

We have developed a novel approach to construct alternative scenarios for stress testing and sensitivity analysis. In this approach, alternative scenarios are constructed as “rules-based deviations” from the baseline assumptions of a stochastic scenario model, followed by manual refinement. This approach is already being successfully applied by some insurers to support their Orsa process.

Has Ortec Finance made any improvements to its technology solutions in the past year?

Apart from the ongoing expansion of economies and asset classes covered in our Economic Scenario Generator, and the advances in the climate and ESG solutions, I would also highlight:

Least Squares Monte Carlo: Based on the unique combination of our frequency domain based Real-World Economic Scenario Generator, and our parsimonious (with few parameters) Risk-Neutral Economic Scenario Generator, we have implemented a new approach for the generation of so-called ‘nested scenario simulations’.

We apply these nested simulations for highly efficient and accurate Least Squares Monte Carlo (LSMC) valuations of insurance contracts in a multi-year stochastic simulation setting.

Cloud Native software development: An approach to software development built on the benefits of cloud computing in which software is split up into smaller manageable units that work together. This allows for fast deployment of high-impact improvements which is important in a fast-changing world.