Matchmade.io claims million-record reconciliation in minutes

Matchmade.io says it can automate the reconciliation of more than one million financial records in under three minutes, positioning the claim against rising payment and systems complexity across Southeast Asia.

Finance teams in the region manage transaction data across banks, payment gateways, marketplaces, point-of-sale systems, enterprise resource planning software and internal databases. That fragmentation has grown as businesses add sales channels and payment methods, leaving many organisations still reliant on spreadsheets and separate workflows to match records and investigate discrepancies.

Its platform reconciles data from those sources in a single system, with accuracy of up to 99%. Matchmade.io says it can also cut the time needed to identify transaction discrepancies by up to 99.5%, with verified outputs then passed into downstream processes such as ERP posting, management dashboards and other automated workflows.

The push for tighter reconciliation reflects a broader operational challenge for digital businesses as transaction volumes rise. Finance departments are under pressure to maintain traceable records across multiple systems without increasing manual work at the same pace.

That challenge is particularly relevant in Southeast Asia, where the digital economy continues to expand. According to the e-Conomy SEA 2025 report by Google, Temasek and Bain & Company, the region's digital economy is expected to surpass USD $300 billion in gross merchandise value in 2025.

In Singapore, the digital economy reached SGD $128.1 billion and contributed 18.6% of gross domestic product in 2024, according to the Singapore Digital Economy Report 2025. The figures point to a business environment generating more transactions across more platforms, increasing the burden on finance operations.

Gilang Gibranthama, Co-Founder of Matchmade.io, said companies are investing in finance infrastructure earlier because complexity is emerging sooner.

"We've seen companies invest in finance infrastructure much earlier than before because operational complexity is arriving much sooner in their growth journey," said Gilang Gibranthama, Co-Founder of Matchmade.io.

He added that the spread of systems is making it harder for finance teams to maintain dependable records for internal use and decision-making.

"As businesses adopt more payment methods, marketplaces and financial systems, maintaining accurate financial data across those systems becomes increasingly challenging. Finance teams need a more scalable way to ensure the data they rely on is accurate, reliable and ready to support business decisions," he said.

AI foundation

Matchmade.io links demand for reconciliation tools to wider adoption of artificial intelligence in finance. As businesses introduce automated reporting, forecasting, anomaly detection and financial analysis, the quality and consistency of source data become more important.

That reflects a growing view in finance technology that AI systems can only be trusted if underlying records are complete, structured and verifiable. In practice, this shifts some spending away from front-end AI applications towards the systems used to collect, match and validate financial information.

Gibranthama said that pattern is already becoming clearer as companies consider how to use AI in finance operations.

"Businesses are moving quickly to adopt AI across finance, but AI is only as reliable as the data behind it," he said. "Before organisations can automate reporting or use AI to support financial decision-making, they first need confidence that their financial records are complete, accurate and consistent."

Enterprise users

Matchmade.io says it works with enterprise clients in retail, financial services and logistics across Indonesia and Singapore. Named users include Bacha Coffee, Pizza Hut, CHAGEE and Wingstop.

The customer list suggests reconciliation software is drawing interest from companies with large numbers of daily transactions, multiple payment channels and several back-office systems. In those environments, finance teams often need to match sales, settlements, fees and bank receipts from different sources before accounts can be finalised.

The business describes itself as a financial data reconciliation platform that matches, verifies and traces transaction records across external and internal systems. It combines rules-based reconciliation with an AI-assisted interface to help finance teams work with fragmented data.

For enterprises dealing with growing transaction volumes, the issue is less about adding another finance tool than reducing the operational risk created by disconnected records. The ability to reconcile high volumes accurately, investigate exceptions quickly and move verified data into accounting and reporting systems is becoming more central to day-to-day finance work.