Andrew Putwain: Can you tell us about stochastic scenario approaches in the wider context of Artificial Intelligence (AI) and the new technologies that Ortec Finance is working on?
Hens Steehouwer: We have two AI streams that we’re working on. The first, like for many other companies, is to improve our own internal efficiency, and the second is to see how AI can improve the solutions that we provide to our investor clients.
This second stream links to our existing scenario approach because the most interesting and most promising application of AI on the solution side is in improving scenario analysis techniques.
The stochastic scenario approach is the workhorse to support strategic investment decision-making as well as the subsequent risk management of strategic decisions over time. The aim is to support effective portfolio decisions and then to monitor whether strategies are still the right ones in changing circumstances.
"The industry is consolidating; fewer, larger organisations are emerging, there is increasing transparency, and more pressure on product margins."
It's been that workhorse for decades in the pension industry and has been growing in the private wealth space as well.
What we have been observing in the last couple of years is that the stochastic scenario approach is increasingly also being adopted by insurers and their asset managers. Our thoughts on this uptake in the industry of insurance relate to the underlying trends that we have been seeing for some time.
Firstly, the industry is consolidating; fewer, larger organisations are emerging, there is increasing transparency, and more pressure on product margins. Furthermore, we’ve also had more than a decade of low rates that are still in the portfolios, and all of this is putting pressure on returns that have to be found. As a result of all of that, we have been observing an increase in the “asset awareness” of the insurance industry and an emphasis on “putting the asset side of the balance sheet to work”.
This increasing importance of the insurer asset portfolios and investment strategies – as an instrument to provide value to the stakeholders of an insurer – is very prevalent. This means the most promising and important application of AI for us, in terms of solutions, is addressing the challenges in creating strategic asset allocations for insurers in the face of a range of, often competing, constraints. That's where the core of our activities are focused on at the moment.
Hamish Bailey: From an asset management perspective, the market dynamic that we observe is a continued move towards outsourcing of mandates by insurers, even by the largest players.
We're also seeing more asset managers pivot towards managing insurance assets as the pensions industry assets start to dwindle or are transferred to insurance companies and insurance structures as the market changes.
As a result of this, we're seeing a lot more competition between asset managers - to prove their added value to clients and to differentiate themselves from their competitors. There's a lot of pressure on their requirements as well.
Adding to the complexity, asset managers have many different clients to manage. The challenge lies in supporting their clients efficiently whilst maintaining personalised touch points and delivering added value. This balance has been a major focus of our work recently.
It's an area where we see the biggest application for AI technologies coming into play.
This interview is the part of a series in our ongoing partnership with Insurance Investor. See the first interview here and the third interview here.
Link to the article: https://www.insuranceinvestor.com/articles/stochastic-modelling-and-ai-what-are-the-new-developments/#_msocom_1
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