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Taxilla AI Transaction Matching automates high-volume matching across bank statements, AR/AP sub-ledgers, payment gateways, and GL - using ML-powered logic that handles fuzzy references, FX tolerances, timing offsets, and complex many-to-many scenarios that ERP rules cannot resolve.
Finance teams spend thousands of hours manually matching transactions that ERPs cannot reconcile automatically - because real-world data never perfectly matches the exact-match logic ERPs are built on.
ERPs rely on exact-match logic that fails with real-world variations - minor reference differences, rounding, timing offsets, and currency conversions all create unmatched exceptions.
Even small variations in amount, date, or reference create large unmatched backlogs that teams must work through manually - often under month-end time pressure.
Finance analysts match transactions line-by-line during period-end close - the most time-critical and stressful point in the close cycle, when errors are most likely.
Unmatched transactions delay account reconciliation sign-off - creating a cascade that pushes the entire close cycle out by days.
Manual matching decisions and overrides are made without systematic documentation - creating audit exposure when matching logic cannot be explained or reproduced.
Cross-currency transactions require FX tolerance matching that no manual process can apply consistently - creating FX-related exceptions that have no systemic resolution.
Bank statements, AR/AP ledgers, payment gateways, and ERP GL data pulled into a unified matching layer - no manual uploads.
ML-powered matching using fuzzy logic, amount tolerances, date ranges, and reference pattern recognition - achieving 99%+ auto-match rates.
Business-defined matching rules for one-to-one, one-to-many, and many-to-many scenarios - configured by finance users, no coding required.
Unmatched items automatically routed to prioritised exception queues with AI-suggested matches and reviewer workflows.
Matched transactions flow directly into Account Reconciliation - clean balances for faster period-end sign-off.
We will demonstrate matching across your bank, sub-ledger, and GL data - showing match rates, exception queues, and audit trail in a live walkthrough.
Bank statements, AR/AP ledgers, payment gateways, lockboxes, and multiple ERP GLs - ingested into a single matching layer without middleware.
Combines machine learning with configurable business rules - the AI learns from approved matches over time, continuously improving auto-match rates.
Amount tolerances, date offset windows, reference similarity matching, and pattern recognition - handling the real-world data inconsistencies that exact-match rules cannot.
One-to-one, one-to-many, and many-to-many matching across high-volume transaction sets - configured by finance users without custom development.
The system learns from prior approvals and overrides - improving match accuracy over successive close cycles without manual rule maintenance.
Unmatched items prioritised, routed, and tracked - with clear ownership, SLA monitoring, and escalation rules for unresolved exceptions.
Within 2 close cycles as the AI engine learns entity posting patterns, reference conventions, and timing behaviours.
Human reviewers focus exclusively on genuine exceptions - not routine matching that can be automated.
Transactions matched as they arrive - eliminating the month-end exception backlog that delays reconciliation sign-off.
Every match decision, tolerance applied, and manual override is fully documented - auditors can trace any matched pair back to source.
All matching logic configured by finance users in the Taxilla platform - no ERP customisation, no IT dependency for rule changes.
Cross-currency transaction pairs matched with configurable FX tolerance bands - CTA differences isolated and explained automatically.
AI Transaction Matching works standalone or as part of the complete Financial Close platform - connecting clean matched transactions directly into Account Reconciliation.
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