Transaction matching is a core part of financial reconciliation. It automates the comparison of transactions across systems such as internal ledgers and bank statements to identify discrepancies, ensure accuracy, and provide a reliable audit trail.
Transaction matching is often the unsung bottleneck in the reconciliation process. For finance teams managing growing transaction volumes, fragmented systems, and tight close timelines, it is a recurring source of manual effort and hidden risk.
Reconciling financial records across internal systems and external sources such as bank statements, subledgers, and intercompany ledgers can become a sprawling task. Especially in multi-entity environments, this process frequently breaks down into spreadsheet-driven workarounds, inconsistent rules, and delayed exception handling.
When handled manually, comparing records can feel like a never-ending loop of downloading files, cleaning data, and adjusting mismatches one line at a time. But there is a more effective way to handle it.
Keep reading to learn how modern finance teams are automating this process to reduce risk, speed up their close, and strengthen audit readiness.
What Is Transaction Matching?
At its core, this process involves automatically comparing individual entries from two or more sources to confirm alignment. These sources can include general ledgers, bank feeds, subledgers for accounts receivable or payable, and intercompany records. The goal is to verify that what has been recorded in one system is reflected accurately in another.
For example, a customer payment that appears in a bank statement should match an open invoice in the accounts receivable ledger. A journal entry recorded in a subsidiary's ERP should match a corresponding entry in the parent company's intercompany ledger.
The process goes beyond simple checks. Matching logic can account for timing differences, reference mismatches, or amount variances within defined tolerances. When these comparisons are handled automatically, finance teams can shift their focus to the few exceptions that genuinely need review.
Recommended read: Intercompany Reconciliation: How to Eliminate Backlogs and Speed Up Month-End Close
How Transaction Matching Works
Behind every clean close is a structured process that ensures transactions are verified, matched, and reconciled with minimal friction. Here’s how the core mechanics of modern matching work, from ingesting raw data to resolving edge-case exceptions.
1. Data Ingestion
The first step is bringing together transactional data from multiple sources. These typically include the general ledger, bank statements, subledgers for AR and AP, cash applications, and intercompany systems. In legacy processes, this data is often manually extracted, cleaned, and formatted before it can be matched.
Nominal eliminates this step. It connects directly to ERPs, banks, and third-party platforms to ingest data in real-time or on a defined schedule. It standardizes formats automatically, even across different file structures or entity configurations. This enables continuous reconciliation rather than relying on periodic batch jobs.
2. Rule-Based Matching Logic
Once data is ingested, it runs through configurable rules. These can include exact matches based on amount and date, fuzzy logic that accounts for typographical errors or transpositions, and tolerances for minor differences. Rules can be tailored for different transaction types or entity-specific processes.
Match types include:
- One-to-one: A single transaction matches exactly with another
- One to many: One payment clears multiple invoices
- Many to one: Multiple payments are applied to a single invoice
- Many-to-many: Complex netting scenarios
Traditional systems require manual maintenance of these rules. Nominal uses AI to learn from past matches and exceptions, continuously improving rule application over time. This reduces noise and increases the auto-match rate, allowing teams to review only unresolved exceptions.
Related post: Agentic AI in Accounting: How Finance Teams Are Automating With Intelligence
3. Exception Management and Resolution
Even with strong rules in place, some records will not align automatically. These fall into an exception queue for review. The key to effective exception management is context. Nominal provides full visibility into the source entries, reason for the mismatch, and potential resolution paths.
Users can accept suggested matches, adjust rules, or add comments and documentation directly within the platform. Once resolved, these actions are logged and auditable, contributing to a complete reconciliation trail. Over time, exceptions become rarer as the platform learns and adapts.
Common Challenges in Traditional Matching Processes
Many finance teams rely on spreadsheets or rigid ERP modules to manage this task. These approaches often introduce more problems than they solve.
- Data fragmentation: Records live in different systems with inconsistent formats
- Manual work: Downloading, cleaning, and comparing files consumes time
- Exception overload: Without intelligent rules, too many mismatches require human review
- Audit risk: Lack of traceability makes it difficult to explain or justify decisions
- Limited scalability: As transaction volume grows, so do delays and errors
These issues are amplified in multi-entity organizations. When subsidiaries use different ERPs or currencies, aligning records becomes a time-consuming puzzle. Reconciliation processes that depend on manual steps simply cannot keep up.
Still matching transactions manually? Download our white paper to see how finance teams automate consolidations, reconciliations, and audit workflows — and close faster without changing their ERP.
How Nominal Automates Transaction Matching Across Systems
Nominal is designed for complexity. It handles high-volume, high-variance environments with intelligence and speed. Rather than force teams to configure static rules inside each ERP, the software acts as an orchestration layer across systems.
Key advantages include:
- AI-enhanced matching logic that learns from historical data and improves continuously
- Real-time ingestion and comparison of records from multiple sources
- Explainable exception surfacing that reduces review effort while improving clarity
- Audit-ready traceability with full documentation and action logs
- Cross-entity and multi-ERP support without requiring a system migration
Consider a scenario where one entity issues an invoice from NetSuite, and the corresponding payment is recorded in SAP by a sibling company. Nominal ingests both records, applies currency conversion if needed, and matches the entries automatically. If a discrepancy remains, it flags the exception, surfaces context, and prompts resolution with one click.
This is not a bolt-on to ERP. It is a finance native layer built to reduce the burden of reconciliation while strengthening oversight.
Matching vs Reconciliation: Understanding the Distinction
While closely related, these types of matching and reconciliation are not interchangeable.
- Matching is the granular process of confirming that individual records align across systems
- Reconciliation is the broader process of validating that account balances are correct and supported
This comparison process is a critical input to reconciliation. Without reliable, automated tools, the overall workflow becomes more manual, time-consuming, and error-prone. By automating this layer, Nominal improves both the speed and quality of reconciliations.
Where Traditional Tools Fall Short
Many finance teams rely on legacy reconciliation tools or ERP native modules. These systems offer some level of automation but often struggle when faced with real-world complexity.
Nominal eliminates these constraints. It is:
- Built for scale: Handles thousands of transactions across entities and systems
- Built for flexibility: Supports different currencies, formats, and structures out of the box
- Built for finance: Prioritizes explainability, auditability, and control without needing IT involvement
This layer of automation is not just a reconciliation task. It is a foundation for accurate financial reporting and a faster close. Nominal elevates it from a tedious back-office function to a streamlined, intelligent process that supports strategic finance.
You might also like: Inventory Reconciliation: Methods, Errors, and How to Automate
Transaction matching, when automated and integrated into a broader reconciliation strategy, transforms how finance teams operate. It reduces time spent on manual tasks, limits exposure to risk, and accelerates the close. For multi-entity organizations working across systems, currencies, and jurisdictions, this capability is not just helpful, it is foundational.
This is the intelligence finance teams need to scale confidently and close faster, without compromising on control or auditability.
Ready to simplify your reconciliation process? Book a demo to see how the platform handles the complexity so your team can focus on what matters most.
