From Matching Transactions to Managing Exceptions: Rethinking the Financial Close

For many finance teams, the close still means manual work: comparing transactions, investigating differences and resolving exceptions across multiple systems and data sources. As volumes grow and environments get more complex, that approach stops scaling. Manual work also takes time at the exact moment teams are under the most pressure to deliver accurate results.

Finance can change when and how this work gets done. Instead of waiting for period end to find discrepancies, teams can move reconciliation earlier in the cycle. They can match transactions continuously as they occur and send people only to the exceptions that need investigation. That cuts routine manual effort, keeps issues from piling up at close, and frees time for analysis.

Automating high-volume comparisons and surfacing exceptions for review lets finance organizations shift from a reactive, period-end exercise to a continuous, controlled and efficient close.

Transaction matching is an important building block of the Record-to-Report process. Matching compares transactions from different sources using defined business rules to identify corresponding entries and highlight items that need further investigation.

Typical use cases include bank reconciliation, intercompany reconciliation, invoice-to-receipt matching, and corporate credit-card transactions. The right use cases depend on an organization’s processes, transaction characteristics, and data availability.

The benefits are especially clear when transaction volumes are high or data comes from multiple sources. But automation requires more than comparing two sets of records. Three foundations must be in place:

• Data must be sufficiently consistent and complete.
• Matching rules must reflect business requirements, including tolerances and different transaction combinations.
• Users need enough transaction-level detail to understand results and investigate unmatched items.

When these foundations are in place, automated matching can reduce repetitive work and help finance teams focus on exceptions rather than routine comparisons.

Timing matters, too. Running reconciliation processes throughout the period—rather than relying only on period-end activities—can help teams identify discrepancies earlier and reduce the work concentrated at close. How far this approach can go depends on data availability, process design, and operational requirements.

Ultimately, the value of transaction matching is not simply that more transactions are matched automatically. It is that finance teams can spend less time finding differences and more time understanding and resolving the exceptions that matter.

From an implementation perspective, successful transaction matching starts with the business process and the data—not with matching rules alone. Two projects illustrate how this approach can address different reconciliation challenges.

Harmonizing data before matching

A major European specialist retailer needed to reconcile collections recorded on its e-commerce website with payments processed by six payment service providers.

The process involved several hundred thousand transactions per day, with additional peaks during certain periods. The collection system and payment providers supplied data in different file formats, so harmonizing the data was essential before reliable matching could take place.

The first implementation phase covered 10 source files. Using OneStream’s embedded extract, transform, and load capabilities, we established a process to standardize the data before applying matching rules. Data loading and matching execution were designed to run automatically, allowing finance teams to focus on unmatched transactions instead of preparing and comparing files manually.

Across a number of implementations, reported automatic matching rates ranged from approximately 95% to 98%, depending on the project scope and reconciliation process. In the e-commerce project, several hours of spreadsheet-based preparation were eliminated per reconciliation cycle. The process could also handle volumes that had been substantially more difficult to manage with the previous Excel-based approach.

The lesson is straightforward: Matching performance depends on both the quality and consistency of the data and the relevance of the matching rules.

Making discrepancies explainable

A second project involved an international animal health group that wanted to improve intercompany reconciliation ahead of financial close.

The existing process relied on Excel files and could result in balances being aligned without enough supporting detail to explain the differences. The finance team needed better visibility into individual transactions, clearer identification of discrepancies, and a structured validation and submission process.

The implementation introduced transaction-level reconciliation, multiple matching rules, and dedicated workflows for different reconciliation requirements. Data enrichment was also important. Where transaction-currency amounts were missing, accounting-currency amounts and daily exchange rates were used to reconstruct the required values. Additional business rules derived analytical attributes needed for consolidation.

The resulting process provided greater visibility into transaction details, the matching rules applied, and unmatched items. It also replaced Excel-based reconciliation activities within the implemented scope and supported a volume approaching 1 million transactions every six months.

This project reinforced a second principle: Reconciliation should not be judged solely by whether balances agree. Finance teams also need to understand why differences exist and have the information required to investigate them.

Together, these projects highlight five considerations for organizations planning to automate transaction matching:

• Understand the process before designing the rules. Identify the source systems, business logic, and reasons transactions may remain unmatched.
• Harmonize and enrich data. Resolve differences in file structures and make missing attributes available where business requirements demand them.
• Design rules around real scenarios. One-to-one, many-to-one, one-to-many, many-to-many and one-sided matching may be relevant depending on the reconciliation process.
• Make exceptions explainable. Transaction-level details and visibility into matching results help users investigate discrepancies more effectively.
• Automate the operational process. Automated data loading and execution, supported by appropriate workflows, reduce repetitive tasks and direct attention toward unresolved items.

The goal is not to eliminate human judgment. It is to help finance professionals spend less time on routine processing and more time on investigation, resolution, and control.

Automating transaction matching can remove significant manual effort from the financial close. But there is a risk: if matching is treated as a standalone automation exercise, finance simply replaces one fragmented process with another.

The bigger opportunity comes from looking at transaction matching as part of the end-to-end financial close:

Transaction matching → account reconciliation → journals → consolidation → reporting

These activities are inherently connected. A transaction that cannot be automatically matched may become an exception that needs investigation. That investigation can affect an account reconciliation, trigger an adjusting journal, flow into consolidation and ultimately influence the numbers reported to management and external stakeholders.

When these activities are disconnected, finance teams can spend valuable time moving data, reconciling information between tools and determining where issues originated. Connecting these activities creates a more natural flow of information through the close, with greater visibility from transaction-level detail through to reported results.

This connection is where transaction matching delivers more value. Rather than operating as a point solution, it becomes part of a connected financial close where matching, exceptions, reconciliations, journals, consolidation and reporting work together.

That connected approach is at the heart of the OneStream platform. OneStream brings transaction matching into the broader financial close, connecting transaction-level activity with account reconciliations, journals, consolidation and reporting.

With this approach, finance teams work in one unified process. Data, controls and workflows carry through the close instead of being handed off between disconnected applications. Exceptions identified through transaction matching can become part of the wider close process. Doing so helps teams investigate issues earlier and maintain greater visibility as information moves toward the reported result.

The value is not simply automation at each step. It is connecting those steps so that finance can manage the close as one end-to-end process, with greater efficiency, control and transparency.

Transaction matching looks like a narrow finance problem. Its implications are much broader.

CustomLib’s implementation experience demonstrates how automated matching can support high-volume payment reconciliation, reduce spreadsheet-based preparation and improve the visibility of intercompany discrepancies. That experience also highlights the importance of data harmonization, appropriate matching rules and transaction-level detail.

Automating routine comparisons and surfacing exceptions earlier lets finance teams rethink how work is distributed across the close.

The destination isn’t simply a higher matching rate. It’s a close in which technology increasingly handles routine processing while finance professionals focus their attention on exceptions, judgment and action.

When transaction matching is connected to the broader financial close, that shift extends beyond reconciliation and helps create a continuous, controlled close.

That is the opportunity OneStream brings to finance teams. OneStream unifies transaction matching, account reconciliations, journals, consolidation and reporting on one platform, connecting activities that have traditionally been fragmented across the close. The result is more than automation. Finance teams spend less time managing processes and more time acting on the issues that matter.

Ready to move from matching transactions to managing exceptions? Visit www.customlib.com and www.onestream.com to learn about connecting transaction matching to account reconciliations, journals, consolidation and reporting.

Written by Nick Cox and Nicolas Bendongué.