Asset servicing workflows are an area of the financial system that Snowflake financial expert Samuel Lee called “ripe for disruption,” explaining that it involves “a ton of data, multiple different actors, and a lot of computation requirements!”
“I’m always confident that we can bring a bit of poetry into our financial world,” said Laurent Moscetti, Financial Services Consulting Service Line Leader for EY. “Whatever you do, it is important to realize in the financial world, asset servicing is just one among many moving parts, of balance, meter, melody, and harmony. If you don’t take the poetry of finance into account, you run the risk of yielding to a company of greater expressive power.”
How can financial companies make service tasks easier, faster, and less painful by employing machine learning and other technologies? And how might workflows change as a result of using these new technologies?
To answer these questions, we spoke with Moscetti and his colleague Ajay Bali, Technology Consulting Partner, Digital Emerging Technologies and Data Solutions Leader. Both are headquartered at EY Luxembourg and together wrote the post How Intelligent Workflow Automation Can Help Asset Managers in Cost Optimization and Scale? We also spoke with Christopher J. Napoli, CFA, Data and Analytics & Financial Services Leader for Snowflake.
More data demands scalability
Asset servicing technologies can be instrumental in providing a competitive edge, and adapting to the introduction of new asset classes.
Ajay Bali: Let’s look at asset managers and ask what they typically do. Their typical services include accounting, tax reporting, investment, and client verification (“know your customer” or KYC).
Additional services may include picking up all the workloads of the non-core asset management sector and applying scale over them, but that is a lot of people (and workflows) to contend with.
Christopher Napoli: The data required for clearing and settlement for certain security types are fairly standardized; however, new asset classes create the need for more data and increased collaboration to avoid breaks, financing, and lengthy reconciliation processes.
Laurent Moscetti: It’s important to understand that asset servicing also exists to help an asset manager distribute its fund across various geographies, which basically means that the distribution of that fund needs to comply with a lot of regulation. It’s very complex work to be done, especially when you want to be at the scale of today, which is worldwide.
Bali: As asset management is growing, there are a lot of cost pressures coming in, which means you need to be much more scalable and be more efficient in doing the work.
Automation accompanies human performance
New assets and regulations make data consistency an ongoing challenge. Automation is vital for speed and accuracy, but still lends itself better to a supporting role, with people making the final call.
Napoli: From a technology perspective, some of the solutions used to settle securities date as far back as mainframes. As such, they tend to be hard-coded, closed-end systems that could not keep up with the pace of change in the industry.
From a data perspective, new asset classes and corporate actions present consistency issues in identifying securities, ensuring the reference data matches between counterparties and clearing houses, and trade confirmations are received and processed on all sides of the transaction.
Moscetti: In order to help a client to really implement smart automation, there is a set of options that a client or an institution can look into. This starts with the basics: robotics process automation (RPA), a type of rules-based task software. Very simple to expect the result that you would get.
What we are seeing now is the industry considering where artificial intelligence can be brought in. You need to be extremely careful with which use cases you choose. The implementation of AI in a real-time decision-making expectation has been rather unsuccessful.
AI has been tremendously successful in helping us make the right decisions in a mass-volume situation.
Orchestrating harmonious workflows and data sharing
Asset servicing, as our experts have noted, is not a one-size-fits-all undertaking. Like so much of life, mastery of this undertaking requires a balance of expertise, creativity, and modernization through data sharing.
Moscetti: There are certain situations which do not require an RPA or AI but instead require workflow management: an orchestration. The process of orchestration is to start a process, then transfer it to another actor or another entity or eventually an external party.
At that point, the process can continue and you have an understanding of the time that has been spent on various parts of that process, what the efficiency of that process was, and so on.
That’s why we are really trying to figure out what is the specific use case that [our clients] want to address. That is when we decide whether it’s a simple RPA that you can use, or whether artificial intelligence is going to be as useful.
Napoli: Data sharing and multiparty cooperation should be routine here. New technology can relieve technology and data challenges.
Moscetti: The results are not just in the process of execution but in the results, by client satisfaction.
Bali: Now when we think of data cloud solutions, where you really see the value is in creating bridges, or engines, standing between these data silos, to produce meaningful information in a 360-degree view.
When we think of workflow automation creating value, it is in this independent data layer, which is able to ingest and correlate the data sets.
Moscetti: In the industry of asset servicing and asset management, the different actors are all clients of someone. The beauty is that the asset manager and the asset services are very keen to understand not only the composition of the assets, but also who is the ultimate client. So if you continue down that line, even on the distribution side, that is a goldmine for the asset manager. They see very clearly who has been buying that fund, whether it’s a pension fund, a wealth management company, or a family office. All this data is totally fragmented across these various counterparts. When you have platforms such as Snowflake, this is where you start building the right layers of consolidation and understanding.
Instruments for fine-tuning your performance
Every skill has a tool or set of tools that make a job more feasible, and asset servicing workflows are no exception to that rule. Data is a tool which our experts describe as today’s competitive advantage, and tomorrow’s common good.
Bali: Today, data and AI provide competitive advantage. But will it be a competitive advantage in five years? I think every competitive advantage you have today will eventually become something the industry holds in common.
Moscetti: Today, the asset servicing industry is under tremendous pressure when it comes to cost management and the margin that they can take out of their own business. So they cannot pursue everything at once. They need to be extremely selective on where the value lies and what they can do for their clients. I think this is probably why I see this kind of solution and technology platform as bringing a lot of competitive advantage, and it will take time before this gets commoditized and spread across the asset ecosystem.
Bali: We are always looking into the full suite of tools—a combination of data, cognitive computing, automation, even blockchain. It’s not a competition between these technologies. It’s really about thinking through how we can use these tools together to address any given problem statement.
Napoli: Data tools usually provide the users with a competitive advantage. The operational alpha created for participants is for the common good.
As the industry modernizes and leverages solutions for data sharing and communication via the cloud, data discrepancies and operational disruptions can be reduced and improve both the top and bottom line.
Data will always be an invaluable element of asset servicing
When we think about poetry, we are as unlikely to think about data as we are to think about the financial industry. And yet, poetry is recognizable by its distinctive style and rhythm. Asset servicing falls into the same comfortable rhythms, but it’s those willing to bend to the disruption, to be empowered by data, who will be the data leaders of tomorrow. Like a formal shift in poetry, digital transformation supported by the right technology, processes, and people will mean a shift in insights that sets the leaders of tomorrow apart.