“You can consider Matchimus as an additional team member dedicated to carrying out reconciliation,” Tracey adds.

Operartis uses benchmarks and metrics over marketing hype to empower companies to save their reconciliations team from the drudgery of manual reconciliations

Rather than relying on AI marketing hype, Operartis believes in empowering its clients to measure the exact value they can gain with Matchimus. “We believe in metrics over marketing and so we designed our POC as a free, quick, easy, automated apples to apples comparison,” This means prospective clients can directly measure the benefits they gain with Matchimus and prove out their ROI before making any investment. “We are happy to say that in all our POCs that we have provided so far, we have eliminated most of the manual matching workload and mismatching issues which can occur because of manual mistakes or rule configuration errors.” she adds.

A case in point, a large financial institution was using an in-house system for trade settlement reconciliations which already included some machine learning capabilities. However, employees still had to invest their time in manually matching reconciliations. The firm engaged Operartis to solve the problem. Having Matchimus on their side, the client reduced their manual matching by 65 percent and reduced mismatches by 95 percent. In another instance, a European bank reconciliation achieved a 78 percent reduction in manual matching and a 90 percent reduction in mismatches.

Looking ahead, Operartis looks to expand its expertise in automated exception processing and focuses on automating back-office functionalities that are not yet exposed to machine learning. The company is also in the process of launching an industryfirst benchmark initiative to provide baseline transaction data sets. These will allow prospective clients to measure their existing systems matching capability against those of their industry peers and baseline against Matchimus without the need to even install any software.