A practical guide for banks and fintechs to assess their current reconciliation processes, measure hidden manual matching and decide where AI will pay off.

How a major credit card provider used Matchimus to cut manual matching in its general ledger reconciliation.

A major retailer's expense reconciliation matched 93% with rules. Matchimus lifted it to 98% and cut manual matching by 80%.

How a leading European bank raised reconciliation auto-matching from 79% to 94% with machine learning.

How a Tier 1 bank applied AI matching to its reconciliation and exceptions management, raising match rates from 88% to 95%.

Why rules-based cash reconciliation struggles with growing volumes and digital payments, and how AI matching helps.

Manual matching after the rules run is often a hidden cost. How to tell whether machine learning matching is right for your reconciliations.

Not all AI matching is equal. Which KPIs to compare and how public benchmark data helps you verify vendor claims.