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Expert Opinion: Thorsten Peisl on DTL

Thorsten Peisl, Chief Executive at RISE Financial Technologies, shares his opinion on DTL within the Asset Management Industry.

According to estimates provided by Oliver Wyman, Accenture, McLagan and others, DLT based processes can generate 30% savings from what is spent today. Today the industry spends about 30 billion per year on post-trade services across the entire securities services space, and asset managers by themselves do represent by far the largest portion of that spend, which translates to roughly 10 billion in savings. We did a very detailed cost research study recently for a large Continental European asset manager and an implementation of DLT for them would bring 24-48% cost savings for their settlement activities alone, so the high-level estimates of 30% seem altogether reasonable to us. With large numbers in play on both sides of the equation to drive ROI, you can see why the financial services industry is one of the leading industries for DLT adoption.

The overall impact of DLT across all the functions of an asset manager over time will be very large, as complimentary activities such as exchanges, and the legacy market infrastructures that support them, make the switch over to DLT technologies.

With respect to today’s DLT activities, a well-designed DLT solution will bring benefits across multiple functions and is designed to be as future proof as possible. These activities are taking place now and will deliver considerable impact over the next 1-3 years.

Right now, the use of AI in conjunction with DLT is limited, DLT does not have the huge data sets needed for true AI and we are not building them with DLT, we are rather focused on bringing industry processes into new business models powered by the DLT value proposition. That said, there are a couple of areas where AI as an input or output DLT linkage can make a lot of sense. Identify management is one area, having established an immutable identity record that can be update by AI surveillance of the entity and any risk factors identified provided back would be very insightful.

As the DLT datasets get built out and grow, no doubt you will see more applicability of AI in the DLT landscape.

Click here to read the full article on DTL – Is There Really a Use Case in Asset Management?

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