With the current economic situation, if firms fail to offer their customers helpful financial simulation services they could lose customers now or in the next crisis.
This came from the seventh episode of Kidbrooke’s podcast series, which aims to help financial professionals navigate the digital transformation of their businesses.
The episode was joined by Kidbrooke head of quantitative analytics Edvard Sjögren. Together with its hosts Natalie Burke and Zaliia Gindullina, they discussed how FinTech can help navigate through economic uncertainty. Sjögren also offered insights into how his team develops and calibrates the OutRank solution, the decision-making framework leveraged by Kidbrooke.
Sjögren, who co-founded Kidbrooke in 2011, explained how he came to build Kidbrooke’s financial simulation engine. Having experience advising institutions on risk models, the Kidbrooke founders realised these tools should be made available to retail customers. With the rising proliferation of cloud technology and the drop in cost of technology, they moved away from consulting and developed software for retail clients in the form of cloud-based APIs.
He said, “in terms of the impact, I think it’s quite well summarised in the vision we have as a company, which is a world where everyone can make well educated or informed financial decisions. I think this is done by bringing this tech to everyone.” Kidbrooke offers its technology to financial institutions, large and small, so it can reach as many consumers as possible.
The current economic environment only accentuates the need for retail customers to have access to financial simulation tools. With pressures on their finances, there are bound to be questions raised about how this affects them now and in the long run.
Sjögren stated, “The ability to answer these types of questions from the institution’s point of view is central to building trust with the clients and maintaining that relationship, because sort of failing to do so might mean that they look elsewhere for advice the next time there’s a crisis. Eventually there will be another crisis, unfortunately, they’re likely to come.”
One way firms can help people navigate the market uncertainty is by leveraging a decision making framework. A key part of this is through the utility framework. This is a mathematical concept where the aim is to capture the preferences of an individual in terms of investments and risk, to build a risk appetite. This is then combined with simulation engines and statistical models, as well as financial goals of the individual to build personalised investment advice for a customer.
One of the unique capabilities of Kidbrooke’s software is its holistic nature. Sjögren explained that financial risks are never in isolation, they impact a range of other aspects of lives or financial products. For example, inflation could impact investments as well as mortgages.
He added, “OutRank was built with this in mind. To be able to create, to take into consideration not just one use case, but many use cases and the combined effect of them. And to look at this holistic picture, and also provide different scenarios, such as what will happen if inflation goes this or that way. But also the likelihood or the probability of one scenario or the other materialising, because if one scenario is more likely than another, it should be given more emphasis.”
Natalie Burke posed to Sjögren that some players in the market are sceptical about the customer value the quality of the underlying models bring. In that vein, she asked what the end customers can experience through the quality and granularity of modelling.
First and foremost, Sjögren explained, customers that receive advice that accurately reflects the effect of inflation will feel more confident in their choices and decision making as they will know what they are doing and will trust the advice offered.
He added, “On the other hand, if you’re failing to take inflation accurately into consideration or even mention it as an aspect [customers] might feel uncertainty or they might even feel misled that it wasn’t even a topic being considered.”
Burke also asked Sjögren how he would respond to those that believe people that believe complex modelling slows down the customer model.
Sjögren said, “One could argue that the simpler model is generally faster, right. But with a well thought through design, it’s not necessarily the case that the more sophisticated or accurate model has to impact the speed negatively. And, while it’s true that it will require more computational power, one way of solving it, and the way we like to talk about it at Kidbrooke, is to separate what data or what part of the model needs to be fast, and what needs to be slow.”
To help visualise this, Sjögren explained that if there are thousands of users interacting and asking for advice, each will have unique data in terms of financial goals and savings. This can be changed quickly to meet their quick needs. However, longer-term simulation and statistical models will not need to be quick to change and can be slower for data processing.
To get more insights from Sjögren, listen to the latest podcast here.
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