From Breaks to Resolutions: How AI Speeds Up Reconciliation

On the surface, reconciliation seems straightforward. Matching trades, cash, and positions is a simple concept. In reality, the real drain on time and resources begins after these matches are made: the breaks and everything that follows once they’re flagged.

Every ops team knows the drill: sift through thousands of mismatches, classify them manually, figure out what’s noise and what’s real, and chase down resolutions. It’s repetitive, slow, and increasingly out of sync with the pace of trading.

That’s where AI comes in. Not just to match records faster, but to tackle the actual bottleneck of break resolution.

Why Reconciliation Is So Messy

At its core, reconciliation is about comparing two sets of records to make sure they agree. This sounds simple enough until you factor in timing mismatches, missing identifiers, inconsistent descriptions, different data formats, and transactions booked in bulk on one side but individually on the other. Then layer in numerous asset classes, counterparties, fund admins, and systems. The result is often a huge mess.

Operations teams are stuck spending hours trying to match trades, resolve breaks, and close out books. They’re juggling interest bookings, corporate actions, fees, OTE adjustments, and more, all of which involve vague or unstructured data. You can’t always rely on a rule-based engine to handle this kind of chaos. And when it comes to one-to-many or many-to-many matches? Good luck doing that by hand.

How AI Accelerates the Process

This isn’t about buzzwords or black-box magic. It’s about practical, repeatable automation that solves real problems.

The AI capabilities of the IVP Reconciliation Solution—specifically, the break resolution suggestion engine—are specifically designed to help ops teams close breaks faster, with less manual effort.

Here’s how it works:

• The suggestion engine learns from past actions. Over time, the engine understands your actions, comments, naming conventions, and matching preferences. It doesn’t just apply generic logic. It adapts to your fund’s way of doing things.
• It solves for complex matching scenarios. One-to-many or many-to-one matching? Not a problem. The engine clusters related records and surfaces suggested links automatically.
• It reduces repeat work. Once the engine sees a pattern, it remembers it. That means fewer breaks to classify, fewer clicks to resolve, and more time spent analyzing the exceptions that matter.
• It gets better the more you use it. As the engine trains on your data, its suggestions become more accurate and more useful. It’s not static. It’s built to evolve.

Why AI Matters for Ops Teams

Ultimately, using the AI-powered break resolution suggestion engine allows firms to move from a reactive stance to a proactive one.

Instead of scrambling at the end of the day to close breaks, your team sees issues as they happen and resolves them faster. Instead of rewriting rules or sorting through long exception lists, they work through AI-ranked suggestions with context. Instead of spending 70% of their time cleaning data, they focus on higher-value tasks like risk analysis and client reporting.

And if you’re running this as part of a managed service or outsourced model? That’s even better, because the AI layer adds scale and control without increasing your headcount.

Reconsider How Reconciliation Works

Reconciliation will always be complex. But it doesn’t have to be slow, frustrating, or overly dependent on manual work. The AI features in the IVP Reconciliation Solution—especially the break resolution suggestion engine—are designed to tackle the edge cases, reduce the noise, and help your team move faster.

Want to see it in action? Learn more about the IVP Reconciliation Solution or contact sales@ivp.in to set up a demo.