Where to start with digital transformation in wealth management

With every new variant of Covid, the end of the pandemic continues to move further into the distance. These past two years have shown the cruciality of digital services, and firms no longer have a choice to avoid it if they want to stay successful. The trouble is, where does a firm start?

Kidbrooke founder and CEO Fredrik Davéus has released a new blog outlining the initial requirements that a wealth management firm must meet before starting a digital transformation project. It includes what components of financial analytics to look out for and how a standardised implementation process could work.

First off, the company needs to understand what type of analytics they want, Davéus explained. The answer to this lies with the needs of the customers and employees. This means a company must assess customers and comprehend what information and types of decision-support would best support them.

Once this has been achieved, there are several points to consider. One important question is if the platform projects the entire balance sheet of a customer or just parts of it. Another thing to ask is how important is it for the analytical tool to consider house views and general historical characteristics and consensus views.

Other questions highlighted by Davéus ask how granular and realistic the underlying asset modelling should be, does the existing infrastructure support API integrations, how many customers does it need to support and how will compliance around decision support be handled.

Once these questions have been answered, Davéus explained there are three main building blocks for a digital financial analytics module to learn.

The first of which is scenario generation. This is an element driving all assumptions about future developments of financial markets. These scenario generators can be simplistic, such as only assessing expected returns for specific asset classes, or more complex, for example, leveraging a multi-period stochastic factor-based model capable of reproducing all well-known characteristics of financial returns.

The second building block Davéus outlined was the balance sheet evaluation. This layer translates assumptions created by the scenario generator to customer-specific projections of their portfolios or balance sheets into the future.

Finally, there are the decision-support tools. These are modules producing the proposals and decision-support material constituting the core of the value-add given to a customer. However, if a firm operates a discretionary business model, this component might not be needed, but those creating a self-service proposition that automates decision-making would need a structured way for the machine to generate such output, he said.

One question that is often on the minds of new projects – does there need to be a dedicated development team. Davéus explained the answer is dependent on the size and ambition of a firm. Looking at Kidbrooke’s customers, some larger firms have dedicated teams, and smaller ones rely on outsourcing.

To read the full blog post and get more insights from Davéus, click here.

Kidbrooke have released several blog posts helping those looking to tackle digitalisation. Late last year, it released a video detailing why decision-making modelling might impact the customer experience.

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