Bank reconciliation combines straightforward candidate matches with exceptions such as charges, partial payments, reversals, timing differences, and batch settlements. Volume and staffing vary by organization.
Do not assume a preset match distribution. Establish exact-match coverage, exception categories, and investigation effort from a representative baseline.
An AI-Assisted Reconciliation Workflow
An AI reconciliation agent operates in four steps:
Fetch: Import or fetch transactions and ledger entries through configured, authorized sources. Manual import may still be required when an adapter is unavailable.
Match: Run exact matching first, then similarity-based candidate matching under reviewed thresholds. Measure precision, recall, and high-impact errors instead of assuming a percentage.
Analyze Breaks: For the remaining unmatched transactions, the agent categorizes each break: bank charges (no GL entry needed), timing differences (transaction posted next day), amount mismatches (partial payments), or genuinely unexplained items.
Escalate: Any break above ₹50,000 or above 0.01% of daily volume triggers HITL review. The CFO sees each break with the agent's analysis and recommended action.
Combining Rules and Models
Deterministic rules are valuable for explainable exact matches. Similarity models can propose candidates for messier cases, but those proposals can be wrong and should remain reviewable.
For example, a debit might equal the sum of two ledger entries. A grouping algorithm can propose that relationship; supporting references and policy determine whether a reviewer accepts it.
The Bottom Line
A pilot should report candidate-match coverage, accepted-match precision, exception-review time, late or failed data loads, and financial error severity. Scheduling, staffing, and savings remain organization-specific outcomes.
Topics
Ready to try it?
Explore the public playground, or create an account to evaluate an agent with your own approved data and controls.