AI Transaction Matching & Daily Reconciliations  

Automated transaction matching compares transactions from two or more financial data sources and applies predefined matching criteria to identify records that belong together. Instead of finance teams manually comparing transactions in spreadsheets, matching software can standardize the incoming data, apply matching rules, identify matched items, and send unresolved transactions into an exception workflow.

Process includes:

1. importing transactions from sources such as ERPs, banks, payment providers, POS systems or other financial systems;
2. standardizing and enriching the data;
3. applying matching rules based on criteria such as amounts, dates, references and configured tolerances;
4. identifying matched and unmatched transactions; and
5. routing exceptions for investigation, documentation and resolution.

Trintech supports configurable rules, AI-assisted matching, multi-way matching and exception workflows so finance teams can automate high-volume transaction reconciliation while retaining visibility and control over unresolved items. Get transaction matching guide

Rules-based transaction matching applies predefined criteria to determine which transactions should match, while AI-assisted transaction matching can help identify patterns and recommend matches or matching rules for more complex scenarios.

Trintech combines configurable business rules with AI-assisted capabilities rather than treating AI as a replacement for financial controls. Matching can be aligned with an organization’s defined tolerances and risk requirements, while AI-driven match-rule generation can help reduce reliance on manual rule creation. Trintech’s broader AI approach also emphasizes reviewability, traceability and human control at critical decision points.

Yes. Trintech is designed for enterprise-scale transaction matching, including high-volume and complex matching scenarios across multiple data sources. Matching can support one-to-one, one-to-many, many-to-one and many-to-many relationships rather than requiring every transaction to have a simple one-to-one counterpart.

This is particularly important when a bank settlement represents multiple underlying transactions, when payments include fees or timing differences, or when records must be compared across several financial and operational systems.

Trintech publishes customer examples demonstrating matching at significant scale. Metro Bank, for example, uses Trintech across approximately one million daily transactions, while Abercrombie & Fitch reports automation across more than 150 million transactions annually. Results vary by organization, data quality, configuration and reconciliation scenario.

Trintech applies governance to AI-assisted financial close workflows through configurable controls, human review and traceability. Organizations can define parameters such as thresholds, account types and approval requirements so automation operates within established finance policies and risk tolerances.

Trintech’s broader AI Platform is designed around human-in-the-loop review at critical decision points, traceable actions and outputs, consistent documentation, and governed workflows aligned with internal controls and audit requirements. This allows finance teams to use AI to reduce repetitive work while retaining oversight over material financial decisions.

Transactions that do not meet the configured matching criteria remain unmatched and are treated as exceptions for investigation rather than being automatically forced into a match.

With Trintech, unresolved transactions can be routed through configurable workflows with clear ownership, supporting documentation and an audit trail. Finance teams can then investigate the underlying cause—for example, timing differences, missing records, fees, incorrect amounts or incomplete reference data—and document the resolution.

This exception-based approach allows accounting teams to spend more time investigating genuine discrepancies instead of manually reviewing transactions that can already be matched automatically.

Yes. Transaction matching rules can be configured around the characteristics of different data sources, transaction types and reconciliation processes.

Trintech supports configurable matching logic for simple and complex scenarios, including multi-way matching. Matching can be aligned with criteria and tolerances appropriate to the organization’s process, while AI-assisted capabilities can help identify matching patterns and improve rule creation.

For enterprise finance teams, this flexibility is important because GL-to-bank, payment settlement, payroll, AP and other reconciliations rarely use exactly the same matching logic. 

Trintech supports multi-currency and multi-entity reconciliation through configurable reconciliation rules designed for global financial environments. This enables organisations operating across entities, currencies and ERP environments to standardise reconciliation processes while accounting for differences such as exchange rates and local requirements.

For transaction-level matching, the exact configuration should reflect the data sources, currency treatment and reconciliation requirements involved. Organizations with complex global requirements should validate those scenarios during solution design.

Yes. Trintech can ingest transaction data from multiple financial and operational sources, including ERPs, banks, POS systems, payment providers and external feeds, allowing organizations to automate matching without replacing their existing ERP environment.

Trintech supports ERP connectivity through integration options that include connectors and APIs, while the transaction matching workflow can normalize incoming data before matching. Reconciled items and exceptions can then feed downstream financial-close workflows, helping reduce manual handoffs between reconciliation and close activities.

Trintech can incorporate approvals, sign-offs and audit evidence into transaction matching and reconciliation workflows so organizations can maintain control over how matched items and exceptions are reviewed and resolved.

Depending on the configured workflow, daily reconciliation processes can include role-based responsibilities and sign-offs, while actions, supporting evidence and exception-resolution history remain traceable. Trintech’s operational reconciliation capabilities also capture source data, matching logic, exception history, approvals and resolution evidence to support auditability.

This gives controllers, reviewers and auditors greater visibility into not only whether transactions reconciled, but also how exceptions were investigated and resolved.