Ai, Ml, RPA In Post-Trade Processing … Sure, They Work, But How Do We Reap Operational Benefits?
Video Transcript
AI, machine learning, and robotic process automation are making waves in post-trade processing, but how do these technologies actually deliver operational benefits?
At the recent PostTrade360 Stockholm Conference, industry experts gathered to discuss just that.
For many organizations, RPA has already become a daily tool, automating routine tasks and shifting the focus from manual operations to IT management.
But the real question is, beyond replacing staff with “bot herders,” what practical improvements are we seeing?
The panelists agreed that implementing these technologies isn’t as easy as it might seem.
There’s a learning curve, and sometimes it feels like you’re just trading one set of challenges for another.
However, the long-term potential is significant, especially with machine learning.
Unlike traditional automation, ML systems can continuously update and improve themselves without human intervention, unlocking new efficiencies over time.
The key takeaway? While the industry has focused heavily on automation, there’s still untapped value in investing in machine learning and giving it time to mature.
Patience and ongoing investment could lead to transformative benefits in post-trade operations.
To learn more, visit nova.contemi.com? or contact us at info@contemi.com to? book a demo.
Host – Posttrade360°
Panellists
- Nathalie Zeghmouli, Business Development Director, Europe, Contemi Solutions
- Duncan Cooper, Head of OMNI Digital Services, EMEA and APAC, BNY Mellon
Moderator: Virginie O’shea, Ceo & Founder, Firebrand Research
The session took place at the PostTrade360 Stockholm Conference for the panel titled “Expanding the uses of AI, ML, RPA to include post-trade functions” to discuss-
- How, AI, ML and RPA are different
- Will implementing AI replace humans
- How much is RPA helping
- Is post-trade ready enough to implement AI
- Challenges you can face around data and investments
- What use case in AI can be applied
- What can AI do in terms of risk perspective
Here’s a brief summary of the session.
For many early starters, “robotic process automation” (RPA) is an everyday practice by now. But except for replacing operational staff with IT folks – “bot herders” – what practical change can we see it leading to? Starting out on a sceptical note, this 17-minute session sought to identify the efficient tech paths that are out there after all, and the best ways to tread them.
“I think the thing we’ve all learned is that if you think it’s gonna be easy, it won’t be,” said Duncan Cooper, while Virginie O’Shea shared her observation of a tendency to “end up having more IT staff than operations staff”.
That said, Nathalie Zeghmouli held up the arguments for more long-term patience – and continued investments – in new technology. Over a long time, there is a lot of potential for example in machine learning (ML) but it is in its nature that it will need time after implementation to do the automated “learning” that is in its name.
“Machine learning enables you to update and enhance without human-being intervention,” she said.
“There is a learning curve but it is constant and endless. In the industry, we have very much been focusing on the first part, the automation, but we have underestimated – or at least we have not invested enough yet – in the machine learning and what it can do.”
The article was originally published by PostTrade360.
Nathalie Zeghmouli
Business Development Director
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