Trade Reconciliation Without the Spreadsheets

Hey, I'm James. Let's see how agentic automation transforms trade reconciliation for the better. Every [music] day, trade operations teams in the middle office face a big challenge, reconciling thousands of trades across systems [music] and fixing what doesn't match. A single failed trade typically consumes around an hour of staff time and can cost over a thousand dollars to resolve. These trade breaks slow everything down, increase risk, and keep analysts stuck in spreadsheets instead of solving real problems. Now, let's see UiPath agentic automation in action. UiPath Orchestrator constantly listens for business events. >> [music] >> In this case, the submission of a trade file. Immediately, our agentic process gets to work. An AI agent extracts trade file headers and semantically matches them to the internal format needed for reconciliation. Next, Orchestrator triggers events in external systems, in this case our reconciliation platform, that produces a trade break report. It formats the report and checks for materiality with a built-in decision engine. This is where it gets really interesting. >> [music] >> Any breaks that are detected are sent to our investigation agent. Built with UiPath Agent Builder, it uses UiPath context grounding to classify breaks, retrieve data from internal and external systems, and reasons to identify the most likely root cause of the break. It doesn't stop there. Our agent also recommends the right [music] steps to resolve the issue. Now, in UiPath Action Center, our operations team can review what the agent has found. >> [music] >> They can approve and modify the agent's findings with a single click, control is passed back to Orchestrator to orchestrate the system updates needed to resolve any trade discrepancies. The result? Up to 60% faster investigations, fewer manual touchpoints, and stronger risk controls. This is agentic automation in action, freeing the middle office from reactive investigations to focus on proactive insights. Thanks for watching. >> [music]