Using digital twins in wealth and asset management

I have always been fascinated by digital twins and the potential they offer. There are many examples of companies using them. BMW has partnered with NVIDIA and uses real-time digital twin factories to optimize its production and conduct predictive maintenance. Emirates Team New Zealand uses digital twins to design and test its boats. SpaceX uses a digital twin of the Dragon capsule to monitor and adjust trajectories, loads, and propulsion systems. McKinsey says that companies can achieve ~50% faster time-to-market, ~25% improvement in product quality, and ~10% revenue uplift with digital twins.

Let’s start with some definitions. What is a digital twin, and how does it differ from simulations and standard CAD (Computer-Aided Design)? Simulations are usually limited to one process (i.e., narrow scope) and do not leverage real-time data. In contrast, digital twins are a virtual representation of a real, complete system fed with real-time data, lasting the system’s entire lifecycle. They allow rapid iterations and optimization of the system. The next big thing is linking digital twins to augmented and virtual reality, interconnecting digital twins, to finally creating the enterprise metaverse.

How about we use digital twins in wealth and asset management? I am hesitant to say that we have already been using them for a long time to model portfolios, test investment strategies, and assess the impact of certain events, to name a few examples. A “purist” might say these are more simulations than digital twins. And this is correct in many cases. The backtest of a portfolio is a simulation. But when an asset manager builds models to optimize portfolios daily, using near-real-time data, it’s getting very close to being a digital twin.

Traditional wealth and asset managers have yet to fully leverage the potential of digital twins because their use of data is limited (which is not/less the case for quantitative asset managers), they could leverage more near/real-time data and alternative data, and they could leverage more data across their entire value chain, from market research to portfolio construction, product development, marketing, and sales and distribution.

Many solutions are available to wealth and asset managers to use and leverage more data. But it requires more than tools. It requires technical skills and talent. Investment teams must have developers within their teams to use advanced market research solutions. Product Development teams must learn data science (e.g., Python) to get the best out of markets’, customers’, and competitors’ data. And the IT team must support these platforms.

Another challenge is where to start and how to build digital twins. A mistake might be to try to build a full fledge digital twin at once. It’s better to start small and evolve the first version. A good suggestion is to run hackathons to develop prototypes quickly and test the initial concepts. And the beauty of the hackathon is that you will have a multi-disciplinary team working together with portfolio managers, product development guys, and engineers.

To be successful, wealth and asset managers must make this a Firm objective, driven from the top. They must invest in talent and team upskilling, and ensure the right innovation culture is in place.