Investigate reconciliation breaks faster

What AI actually does here

• Matching platforms use rules and, increasingly, machine learning to suggest matches (vendor claims vary).
• AI can group similar breaks and suggest likely root causes (timing, FX, corporate action, static data) from the break pattern.
• Drafts break commentary and escalation notes for an analyst to approve.

Tools to look at

• Reconciliation platforms such as Duco, SmartStream and Gresham (check each vendor’s current AI features and claims)
• Firm-approved AI assistants for drafting commentary

Named tools are examples, not endorsements. We have no paid relationships with them at the moment; see our disclosure.

What you need first

• Clean, consistently formatted source data and agreed break categories.
• Clear controls: AI suggests, an analyst decides. Keep an audit trail of who accepted what.
• Firm approval for any tool that touches client or position data. Never paste live data into an unapproved public chatbot.

How to measure ROI

• Average age of open breaks and time to resolve, before and after.
• Analyst hours per day on investigation and write-up.
• Accuracy: percentage of AI-suggested causes accepted on review.
• Time saved only counts if it is redeployed (to backlog, controls or growth) or avoids hiring.

Run the numbers in the ROI calculator →

What the evidence says

The Bank of England and FCA’s 2024 survey found operations and IT is the business area with the largest share of AI use cases in UK financial services (around 22%). Vendor efficiency figures should be treated as claims until tested on your own data.

You are assisting a reconciliations analyst. The data below is ANONYMISED test data.

BREAKS:
"""
[paste: break id | account | asset | our value | their value | difference | age in days | any notes]
"""

KNOWN CAUSE CATEGORIES: timing, FX rate, corporate action, fees/commission, static data mismatch, missing trade, duplicate, other.

For each break: suggested category | reasoning in one line | confidence (high/medium/low) | next check the analyst should make.
Then group the breaks by suspected common root cause.
Never state a cause as fact. These are suggestions for human review.

Sources

1. Bank of England and FCA, "Artificial intelligence in UK financial services – 2024" Regulator survey
2. Duco (product page) Vendor page
3. SmartStream (product page) Vendor page
4. Gresham (product page) Vendor page

Last updated 29 October 2026. Spotted something out of date? Email support@aiforthisjob.com. How we research and check playbooks.