Innovation – Digital Transformation of Asset Managers and Wealth Managers

Paradigm shift to replace the legacy technology stack of banks [and wealth and asset managers]

The McKinsey & Company article “Banks’ core technology conundrum reaches an inflection point” presents an insightful perspective on the core technology challenge that banks are currently facing, which is now reaching a critical point. While this issue has been discussed for a long time, two factors are making the situation more pressing than ever. Firstly, banks will soon face a talent shortage in their legacy technologies. At the same time, they will have to fight for talent in new technologies. Both talent shortages will put significant pressure on their ability to maintain and evolve their systems. Secondly, legacy technologies are consuming a growing share of banks’ budgets, leaving them with limited resources to pursue strategic initiatives that can drive innovation and transformation.

Another interesting element discussed in this article is how Thought Machine is thinking about solving part of the problem by running products as code and making them independent of the platform, which can be composed of tens if not hundreds of different systems for incumbent banks: “We have a system of smart contracts that run on the platform, but they’re separate from it” says Paul Taylor, founder and CEO of Thought Machine. Brian Ledbetter, a senior partner at McKinsey & Company, also brings up the concept of putting risk controls in code and not in processes for risk management. After infrastructure as code, which we have been discussing for quite some time, we are adding controls as code and products as code, significant paradigm shifts that are complicated for incumbent banks still dealing with mainframes and systems that are 20+ years old.

The challenge of legacy IT stacks and technical debt for incumbent banks has been discussed for decades. Incumbent banks must not look at this as a systems replacement, but as an enabler and a necessity for their future. To be successful, incumbent banks must educate their business on technology, have a technology talent strategy, and bring people to the center of their digital transformation.

Interesting video from the CEO of Thought Machine.

2023 Gartner Emerging Technologies and Trends Impact Radar

Gartner has released its 2023 Gartner Emerging Technologies and Trends Impact Radar. Let me try to give it a read with the eyes of a wealth
or asset manager.

Artificial Intelligence is all over the radar, with Foundation Models, Self-Supervised Learning, Generative AI, and more. Wealth and asset managers must seriously look into Artificial Intelligence and understand how it can be leveraged across their value chain, from investments to operations, risk management, and compliance. Part of the solution will come from their solution providers, like Bloomberg, BlackRock Aladdin, or State Street Alpha, to name a few providers. But wealth and asset managers cannot only rely on their providers. Instead, they must acquire the necessary skills and talent and play with artificial intelligence technologies. War for talent will make it complicated.

Blockchain is also quite present with Web3 and Tokenization, both being in the 3-6-year horizon. Most wealth and asset managers are already testing tokenization in one way or another, and they should continue. Tokenization will have many benefits for the industry, from speeding up transactions, eliminating some intermediaries and therefore reducing costs, making some asset classes available to smaller investors, improving the liquidity of some assets, and more. Tokenization will require some industry alignment and standards.

No surprise, Digital Twins are here too. Gartner is probably thinking more about Digital Twins in the context of manufacturing and industrial activities. But as discussed in this blog, the potential for digital twins in the financial industry is real and massive.

Then, there are hardware and infrastructure with Neuromorphic Computing, 6G, and Hyperscale Edge Computing. If wealth and asset managers continue to move to the cloud, they’ll be able to leverage these latest hardware technologies as they become available in the cloud.

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.

Let’s look at the digital transformation of other industries – The Washington Post

Looking at other industries to think about innovation and how to leverage technology in wealth management is always interesting. In that context,
the digital transformation journey of the Washington Post led by Shailesh Prakash is very insightful.

They quickly recognized that The Post needed to achieve excellence in both journalism and technology. It was a radical transformation, moving the IT department from an IT system babysitting mindset, IT systems in which the newsroom staff had very little confidence, to a product development mindset, building and inventing digital products. Part of the journey was adopting agile and colocating the engineers with their partners from the newsroom. They decided to build versus buy, set a fast experiment and innovation culture, and developed an obsession with products. Attracting and retaining the best engineers was key in that journey.

Through their excellence in technology, they built a set of tools they could sell to other publishers: Arc Publishing (Arc XP) was born, generating tens of millions of dollars of revenues for The Post.

Deep dive into the digital transformation of the Washington Post with the University of Virginia case study. There are also plenty of resources on the web. Just search for Shailesh Prakash (who is now at Google). If you like this digital journey, I also recommend reading the Goldman Sach’s Digital Journey case study from Harvard Business School. A great read.

Some thoughts on innovation

Trying to put together some thoughts about innovation, I have reread some chapters of “Non-bullshit innovation” from David Rowan, a book I recommend reading. I personally like a lot the chapter about Autodesk, “Find your blind spot” and ARUP, “Empower your team.”

Not trying to be exhaustive and pretending this is “the answer”, here are a few simple principles to be innovative, adapt, and potentially survive:

Fund long-term experiments;
Be obsessed with the future;
Have a lab, change the culture, and show that taking some risks is fine (and necessary);
Get involved by curiosity, not to make some public relationships;
Follow the 3-horizon framework: 1. Maintain today’s core business; 2. Nurture emerging businesses that could become significant; 3. Conceive new future businesses in a more speculative way;
Create organizational tensions that challenge the status quo thinking (link that back to the 3-horizon framework);
Make sure you keep up with the speed of change in the industry. Otherwise, you will fall behind;
Innovation must make it to the real world. Otherwise, it is not innovation;
Tell stories, great stories.

Now, take these nine principles and turn them into questions, e.g., do we have a lab? Do we fund long-term experiments? Are we obsessed with the future? And so on. Where do you stand in terms of innovation?