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DLT and AI Top of Mind at SIFMA Fintech Conference

DLT and AI Top of Mind at SIFMA Fintech Conference

Last week, I attended SIFMA’s Fintech conference in New York. I sat through panels discussing the topics of the day in financial services operations – emerging technologies, the ever-evolving regulatory landscape, you name it. Overall, it was an engaging event with interesting insights into how fintech is transforming the financial services sector. As I reflected on the event and the topics explored, it became clear that many of us face the same challenges in grappling with new technologies that promise to transform the industry. Based on my experience at the conference, here are a few of my observations:

Distributed ledger technology is picking up steam, although there remains a wide spectrum of understanding across the industry

A few distributors have broken through the pilot phase of implementing distributed ledger technology (DLT) at their firms and are looking for production usage. What seems to be the latest hurdle is neither the lack of willing participants nor integration within the existing ecosystem, but legal opinions on the tokenization of securities. I predict that the bureaucratic process will be drawn out as usual. Even though it may take 1-2 years for clear guidelines to be put in place, it will not be held up indefinitely.

While we are starting to see some progress from the first movers, the democratization of DLT knowledge remains in the early stages. I was amazed at the diversity and the level of complexity of questions asked by audience members in some of the sessions. In one instance, someone asked how distributive ledger technology worked and was curious about the level of buzz surrounding the topic. This question was followed by someone asking about using hash graphs for sub-millisecond transaction writes. It’s clear that the industry’s knowledge and uptake of DLT is far from consistent.

The entire industry is dealing with hurdles that are inherent with the nascent nature of fintech – AI being a leading example

Many of the discussions at the conference centered on technology moving out of the lab and into the industry. Through these panels, it became evident that much of the industry is on the same page in terms of the obstacles inhibiting implementation. Two of the most common issues are:

  • Limited resources to achieve the most mature product in a business model: Firms who are looking to bring their innovative technology solutions to the market face the issue of having a limited amount of resources, whether it be time, money, or manpower. To combat this obstacle, some may turn to consortiums to pool their assets. However, consortiums have complex legal, funding, and participant-herding issues and therefore can take eons to build. I’m not saying that teams should not go that route, but my personal experience is that it could take years to achieve results.
  • Pioneering a new tech frontier comes with less control: AI models are software assets that behave differently than anything we have created before — not only from a capability point of view but from a change-control perspective.  Many AI systems have the ability to change themselves without human intervention. Think about that for a moment from an enterprise change-control perspective. What happens if your AI system changes a process where proper documentation of modifications is necessary? As firms begin to implement new technologies into their systems, they’re going to have to determine the best way to maintain control and accountability over the processes.


Overall, the conference confirmed that the issues surrounding Fintech aren’t going away any time soon. With firms now looking to take their projects out into the real world, there are bound to be hiccups along the way. However, once the issues are ironed out, the progress will be well worth the wait.


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