Transaction Matching

 Ai Transaction Matching

AI Transaction Matching Software - Beyond ERP Matching Rules

Automate high-volume transaction matching across bank, payroll, clearing, and intercompany accounts-without manual rules or spreadsheets. Taxilla uses AI-assisted and rules-based matching to reconcile millions of transactions faster, more accurately, and continuously-reducing dependency on manual effort during close.

The Challenge

Manual Matching Doesn't Scale

Finance teams spend thousands of hours manually matching transactions that ERPs cannot reconcile automatically.

Rigid ERP Matching Rules
challenge1

ERPs rely on exact match logic that fails with real-world data variations.

High Exception Volumes
challenge2

Minor timing, rounding, or reference mismatches create large unmatched backlogs.

Manual Effort & Burnout
challenge3

Teams match transactions line-by-line during month-end pressure.

Delayed Reconciliations
challenge4

Matching becomes a bottleneck for account reconciliation and close.

Audit Exposure
challenge4

Inconsistent matching logic and manual overrides weaken audit confidence.

Solution Overview

Built for High-Volume Matching at Scale

Taxilla provides a centralized transaction matching engine that works across banks, sub-ledgers, and intercompany data-feeding clean results directly into reconciliations.

How it Works

Ingest → Match → Review → Resolve → Certify

Fully Automated End-to-End Process

Configure

Ingest Multi-Source Data

Seamlessly ingest transaction data from bank statements, AR/AP sub-ledgers, payment gateways, and multiple ERPs into a unified reconciliation-ready data model.

Ingest & Transform Data

AI-Assisted Matching Engine

Leverage AI-driven matching using fuzzy logic, tolerances, date and amount patterns to automatically identify and pair transactions with high accuracy.

Run the Driver-Based Allocation Engine

Configurable Matching Rules

Configure flexible matching scenarios-including one-to-one, one-to-many, and many-to-many-using business-defined rules without custom coding.

Review & Approve

Exception Management

Unmatched or partially matched items are automatically flagged, prioritized, and routed for review with AI-suggested matches and explanations.

Post & Integrate

Downstream Integration

Approved and matched transactions seamlessly flow into Account Reconciliation, ensuring clean balances and faster period-end close.

Key Benefits

Tangible Outcomes for Your Finance Team

70-85% Auto-Match Rates

Dramatically reduce manual matching effort through AI-driven automation.

Faster Reconciliations

Clean transaction sets accelerate balance-sheet substantiation and close timelines.

Lower Exception Backlogs

AI continuously learns matching patterns and improves accuracy over time.

Audit-Ready Matching Logic

Every match is fully traceable, explainable, and auditor-friendly.

Continuous Matching, Not Month-End Spikes

Shift from reactive month-end processing to proactive, ongoing reconciliation.

Standardized Controls Across Entities & Systems

Enforce consistent reconciliation controls across ERPs, entities, and shared services.

Module Capabilities

Features Deep-Dive

Driver-Based Cost Allocation Engine

Multi-Source Transaction Ingestion

Ingest transactions from bank statements, AR/AP ledgers, payment gateways, lockboxes, and multiple ERPs into a unified matching layer.

Rule-Based Allocation Framework

AI-Assisted & Rules-Based Matching

Combine machine learning with configurable business rules to achieve high auto-match rates without manual intervention.

Intercompany Chargeback & Billing Automation

Tolerance & Fuzzy Logic Matching

Match transactions using amount tolerances, date ranges, reference similarity, and pattern recognition to handle real-world data inconsistencies.

Multi-Layer Workflow Collaboration

Complex Matching Scenarios

Support one-to-one, one-to-many, and many-to-many matching scenarios across high-volume transaction sets.

Multi-Layer Workflow Collaboration

Learning-Based Match Recommendations

  • The system learns from prior approvals and continuously improves match suggestions over time.
  • This transforms transaction matching from a manual comparison exercise into an intelligent, self-improving automation layer.
Multi-Layer Workflow Collaboration

Exception Queues & Reviewer Workflows

Unmatched or partially matched items are automatically routed into prioritized exception queues with reviewer actions.

Technology Advantage

Engineered For Enterprise Scale

  • ERP-Agnostic Architecture

    ERP-Agnostic Transaction Engine

    Seamlessly integrates with SAP, Oracle, NetSuite, Dynamics, and hybrid ERP landscapes without disrupting existing accounting systems.

  • Continuous Processing Model

    Continuous Matching & Reconciliation Model

    Transactions are matched and reconciled continuously eliminating month-end spikes and enabling a true continuous close.

  • Unified Intercompany Sub-Ledger

    Scalable, Rules-Driven Configuration

    Business users configure matching rules, tolerances, and workflows without ERP customization or IT dependency.

  • Unified Intercompany Sub-Ledger

    Audit-First Data Lineage

    Every transaction, match decision, override, and approval is fully traceable from source to close.

  • No Spreadsheets. No Custom ERP Code

    No Spreadsheets. No Custom ERP Code.

    All logic runs in a secure sub-ledger layer-keeping ERPs clean while enabling enterprise-grade automation.

TechnologyAdvantage
Integrations

Seamless Integrated Across Your Ecosystem

Taxilla integrates with:

ERPs

ERPs

Operational Sources

Operational Sources
Industry Use cases

Trusted By Global Shared Services

Automated shared services chargebacks across 30+ legal entities, reducing disputes by 80% and accelerating close by 5 days.

Global Shared Services Center

Standardized high-volume transaction matching across banks, AR/AP, and payment systems-achieving 75-85% auto-match rates and reducing manual effort by 60%.

Retail & Consumer Goods

Automated bank and payment reconciliation across stores, regions, and gateways-shortening cash visibility cycles and cutting close by up to 4 days.

Manufacturing Enterprise

Matched complex one-to-many and many-to-many transactions across customers, vendors, and plants-improving audit confidence and reducing reconciliation exceptions.

Technology & SaaS Company

Enabled continuous transaction matching across multiple entities and currencies, supporting faster monthly closes and scalable growth.

Pricing & Plans

Transparent Pricing for Every Stage of Growth

Taxilla pricing for AI Transaction Matching scales based on transaction volume, matching complexity, and entity count, ensuring predictable costs and rapid ROI-without spreadsheet dependency or ERP customization.

Essentials

Mid-market teams

  • Up to 5 entities
  • Up to 100k transactions/month
  • One-to-One Matching Scenarios
  • Rule-based Matching Logic
  • Limited Tolerance & Fuzzy Matching
  • Basic Queues Exception Management
  • Limited Evidence Attachments
  • Basic Analytics & Match Metrics
  • Email support
  • Close Task Integration
  • Multi-currency & FX variance engine
  • AI-Based Match Recommendations
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Enterprise

Global Enterprise

  • Up to 40+ entities
  • Unlimited transactions/month
  • Many-to-many & complex Matching Scenarios
  • AI-assisted + learning Matching Logic
  • Advanced Tolerance & Fuzzy Matching
  • Multi-level approvals Exception Management
  • Centralized repository Evidence Attachments
  • Executive dashboards Analytics & Match Metrics
  • Dedicated CSM support
  • Close Task Integration
  • Multi-currency & FX variance engine
  • AI-Based Match Recommendations
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FAQ's

Frequently Asked Questions

Does AI Transaction Matching replace ERP matching functionality?
No. Taxilla complements ERP capabilities rather than replacing them. ERPs are designed to record transactions, while Taxilla operates as an intelligent matching and control layer that handles complex, high-volume, and cross-system matching scenarios ERPs struggle with.
How does the AI explain and justify each match?
Every match is fully explainable and auditable. The system records the rules applied, tolerances used, confidence scores, and any user approvals-ensuring complete transparency for auditors and internal controls.
Can the system learn and improve matching accuracy over time?
Yes. Taxilla uses pattern-based learning from prior approvals and overrides to continuously refine match recommendations and improve auto-match rates over successive close cycles.
What types of matching scenarios are supported?
The platform supports one-to-one, one-to-many, and many-to-many matching across bank, AR/AP, payment, and sub-ledger data-handling real-world reconciliation complexity at scale.
How are unmatched or partially matched transactions handled?
Unmatched items are automatically routed into prioritized exception queues with suggested matches, reviewer workflows, and clear ownership-ensuring faster resolution without manual tracking.
Is this suitable for audit-intensive and regulated environments?
Yes. The solution is built with audit-first controls including evidence attachments, approval workflows, full data lineage, and period-end certifications aligned with audit and compliance requirements.

Move beyond ERP limitations and spreadsheets.

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