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Tracey Lall, Director of Operartis“When we started out, we went back to ground zero and looked at the matching problem from a mathematical perspective,” says Tracey. After years of R&D, the company developed Matchimus, an automated reconciliation solution that combines machine learning and optimization algorithms to provide advanced match intelligence. “Matchimus automates the matching process for any type of reconciliation while providing groundbreaking match rates and accuracy that allows skilled workers to spend their time on value-added activities instead.” says Tracey, Director of Operartis.
Companies can integrate Matchimus into their existing systems as an add-on module without disturbing their existing systems and business workflows. The solution is installed in minutes in a plug and play format, and the data integrated within a few hours. Once trained on the existing data, Matchimus picks up the game and makes its own decisions on the new incoming transactions.
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.
Operartis uses benchmarks and metrics over marketing hype to empower companies to save their reconciliations team from the drudgery of manual reconciliations
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.
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Company
Operartis, LLC
Headquarters
New York, NY
Management
Tracey Lall, Director of Operartis
Description
Founded in New York by veterans of the banking industry the firm’s mission is to provide innovative technology solutions which push automation and straight-through processing to the next level, driving down the need for tedious manual work and freeing up the most precious resource an organization has: its people. Their international team located in New York, London and Barcelona includes banking IT veterans, PhD data scientists and enterprise architects, providing the ideal combination of academic know-how, system performance and grounded operational understanding
