What are the key issues associated with manual data reconciliation processes?
Historically, the technology and processes to support manual data reconciliations have been very much geared towards addressing challenges around sharing the data, looking at the data and then using very linear technologies over the top. But a series of trades doesn’t simply add up to a position. It’s a very simple statement, but an important one.
This is compounded by a couple factors -the increase in transaction volumes and more complex assets being transacted. This is leading to frequent errors (due to different systems and non-linear processes) resulting in a costly mess of point-to-point reconciliations and mistakes.
We really need a very different approach than the one that exists now. One long-term solution to this is tokenisation and digitisation, where everyone is looking at the same record. But even then, that’s not enough on its own. The optimum state is having the ability to make a summation of those records by using non-linear enabled technologies which can be aligned to clear business processes.
How can new data management technologies help reduce operating costs?
It’s important to note how new data management technologies can not only reduce operating costs, but also reduce risk. This means individuals can be granted permission to access only the specific data they need therefore ensuring compliance with market data licensing and regulatory obligations while being authorised to perform the right action. Therefore, creating a safer environment: people can then see the bits of the puzzle they need for their specific role, without necessarily seeing all of it.
Again, the issue with the push towards tokenisation and digitisation, means a lot of the public blockchains have access to everything. So that’s not quite the right solution. It’s about finding the balance which reduces duplication, synchronisation issues, translation problems, and breaks, while maintaining the controls required for financial services.
What is the key driver behind the push for real-time data sharing?
It’s largely driven by the increased sophistication of the ultimate capital allocators. If you look at sovereign wealth funds and pension funds, as a macro trend, they’re starting to insource more of their own risk management. In order to do that, they need to look at the same data.
So, they’re starting to ask their managers and their custodians and administrators for data that’s at the same time point. When quick decisions are necessary, relying on data that’s 90 days old here, 30 days old there, or even just a few days outdated can hinder the process. The goal is to have all relevant information in real time to enable better, more informed decisions on a macro level. We’ve seen that trend an awful lot more where the end capital owners are becoming much more sophisticated in their approach.
What are the main pressure points for asset managers when it comes to their operating models?
There’s a change in the asset mix with the end allocators wanting both public and private assets. There is also a change in the demand for data with asset managers wanting all their data in real-time. For allocators, fee compression is a significant concern. As more machine learning, AI, and automation are introduced, they may ask, “Why should I pay 1.5% or 2% in fees when I could be paying just 0.5%?” Additionally, with interest rates staying relatively high, investors may question, “Why take on risk to earn 7% when I can get 5% or 6% from government bonds – and avoid paying you a 2% fee?”
This combination of factors has created a perfect storm: capital flight from traditional managers, pressure to lower fees when they do secure capital, and an increased demand for complex asset mixes. All of this contributes to a significant challenge for the profitability of asset managers.