Ortec Finance: Models improve decision-making
How has Ortec Finance helped its clients during the Covid-19 pandemic?
Hens Steehouwer: In times of crisis, we traditionally take extra care by staying in touch with our customers to best assist them.
As an example of our client-centric approach, we have started hosting a quarterly scenario webinar series to update clients on the latest insights from our scenario models as the pandemic evolves.
We have taken extra care in monitoring the performance of our scenario models and incorporating information about the pandemic.
For example, our research has shown that the events of the first quarter of 2020 materialised in the worst 1% of the probability distributions projected at the start of the year. This has given us comfort that our scenarios are able to capture extreme and unexpected events, such as the Covid-19 pandemic.
The remainder of the year has moved back nicely into the centre of the distributions.
Furthermore, as early as Q1 last year, we began developing a special set of Covid-19 stress scenarios with our partner Cambridge Econometrics to help our clients test the robustness of their portfolios.
During Q1 and Q2 in 2020, these stress scenarios were used by several clients and included, for instance, in their Orsas.
How can Ortec Finance help insurers address climate risk?
Hens Steehouwer: Based on collaboration with our partner, Cambridge Econometrics, our scenarios can incorporate climate risks and opportunities associated with different global warming pathways.
Insurers can run these scenarios through their internal models, or our asset liability management models, to assess the potential impact on their balance sheets and to fulfil TCFD reporting needs.
The scenarios include both risks and opportunities related to the transition to a less carbon intensive world, as well as physical risks related to the impact of rising temperatures and changing precipitation patterns.
Climate risk means there will be winners and losers, in terms of countries and sectors, and therefore the impact will be different for every insurer.
Increasing physical risks from climate change are likely to have a negative impact on many countries and sectors, and cannot be avoided through diversification.
Post-Covid-19, how will insurance capital and risk modelling change?
Frido Rolloos: It seems likely we will remain in a low-yield environment for quite some time, while at the same time shareholders and policyholders will continue to demand returns.
The resulting search for yield will push insurers further into considering credit and alternative assets as part of their investment portfolios. However, with the increased yield in the credit market also comes increased rating migration and default risk.
These will have to be integrated into capital and risk modelling in a realistic way, and hence our technological focus on stochastic rating transition matrices.
We also expect a need from the industry for more frequent analyses of realistic balance sheet projections backed by robust economic and climate scenarios that are up to date with the latest available information.
Just carrying out analyses of balance sheet evolution through different scenarios, however, will be insufficient. Organisations will also have to be able to act quickly to different projected outcomes.
This can only be done of course if the ALM process and platform is modular and flexible enough that it can be embedded across the different functions of an insurance company – such as investments, risk and actuarial – and thereby enable the different functions to work on a consistent and shared set of information.
What are Ortec Finance’s technology focus areas for 2021?
Frido Rolloos: With the continuation of the pandemic, we think monitoring downgrade risks in debt portfolios will become increasingly important.
In addition, we see an increased demand for managing cashflow-driven investment strategies. It is for these two reasons that in 2021 we will release a new module, which extends our credit risk model with stochastic rating transition matrices.
We also expect to extend our API functionality and support the continued need for automation and process efficiency at insurance companies.
