A practical guide for reconciliation teams looking to raise automated match rates with machine learning, including how to calculate the ROI.

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

When you need machine learning for transaction matching, how it works, which KPIs to compare vendors on and how public benchmark data helps.

How a team of finance and technology specialists set out to fix manual reconciliation, and built Matchimus.

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

How Matchimus uses machine learning and calibrated confidence scores to match imperfect data that rules miss.