Industry
Payments and Financial Services
Automating Transaction Reconciliation with ML-Driven Data Processing
in reconciliation effort
in transactions enabled annually
transactions processed
A global payments product network needed to modernize the reconciliation of Travel Management Company transactions with financial data. Its manual, error-prone matching process created delays and required significant operational effort.
Altimetrik implemented an automated, ML-driven data-processing solution that consolidated transaction data, identified matching patterns, and reconciled records at scale. The solution reduced reconciliation effort by 80% while enabling $0.6 billion in annual transactions.
The client manually reconciled TMC transactions with invoice and financial data. Differences in partner formats, transaction details, and matching patterns made the process slow and prone to errors.
The organization needed to automate data ingestion and matching, manage unresolved records efficiently, and generate reliable insights for partner offerings and operational decisions.
Delayed or inaccurate reconciliation affected transaction visibility, financial reporting, and partner operations. As transaction volumes increased, the manual approach became difficult and costly to scale.
Altimetrik analyzed the existing business process to identify recurring patterns and automation opportunities.
Reusable pipelines extracted and consolidated TMC, transaction, invoice, and financial data. Automated ETL processes aggregated this information and identified patterns before sending records to the matching engine.
An auto-match engine applied established global rules to reconcile transactions. Unmatched records were routed through a Spark-based processing layer using dynamic rules from a centralized repository. ML-powered fuzzy-matching algorithms then identified relationships that traditional exact-match rules could not detect.
Matched records were processed through automated workflows, while unresolved transactions were directed to matching tools or third-party outreach. New matching patterns were added to the rules repository, enabling continuous improvement.
Lowered manual reconciliation effort by 80% through automated matching, dynamic rules, and ML-driven pattern identification.
Processed more than 285 TMC transactions while consolidating financial and partner data through reusable pipelines.
Improved matching accuracy, accelerated exception handling, and created better analytics for partner offerings and operational decisions.
Altimetrik combined payments expertise, data engineering, and advanced analytics to deliver intelligent transaction reconciliation at scale.
“Amit Singh is the Chief Strategy Officer and Chief of Staff to the CEO at Altimetrik, where he drives corporate strategy, growth acceleration, and value creation through transformation initiatives. In this dual role, he partners closely with leadership teams, investors, and the board to align business strategy with sustained, technology-driven growth.
With over two decades of experience at the intersection of technology, business, and transformation, Amit brings a unique perspective on how organizations can innovate and adapt in a rapidly evolving digital landscape. His career has been defined by building high-performing teams, scaling innovative platforms, and driving organizational change to deliver lasting impact.
Before joining Altimetrik, Amit held senior leadership roles at Visa, where he led technology strategy, engineering, and product development for Real-Time Payments and the Visa Developer Platform. Earlier, he served as Chief Product Officer at a startup and spent more than a decade at Oracle, leading product and engineering teams across a wide range of enterprise software applications.”
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