Take a look at the highlights from our recent Data Management Summit in London.
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Data is the fuel that keeps modern financial institutions’ motors running but if that data can’t be trusted then the decisions made based upon it, or the uses to which its put, will be compromised. That’s especially important for data that’s fed into artificial intelligence models. If the data isn’t clean, accurate and complete, then...
Reconciling fragmented financial data across disparate trading systems, risk engines and regulatory reporting pipelines is a challenge that frequently results in conflicting analytical outputs, costly reconciliation cycles, and elevated operational risk under frameworks like BCBS 239 and FRTB. The enterprise response has been a move away from continuous database consolidations towards centralised semantic layers. Positioned...
Now in its 16th year, Data Management Summit (DMS) NYC returns on September 17th 2026, to explore how to use data and AI to drive measurable business outcomes – reliably, repeatedly and at scale.
AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...