AI-assisted transaction reconciliation overview

AI-assisted transaction reconciliation compares ERP transactions with Avalara AvaTax records, identifies discrepancies, and guides you through resolving them.

AI-assisted transaction reconciliation illustrates how ERP transactions could be compared with Avalara AvaTax records to identify discrepancies and guide users through resolution.

This guide describes how an AI-assisted reconciliation approach could help maintain data integrity between an organization’s ERP system and Avalara AvaTax.

Currently, existing integrations support reconciliation workflows using AvaTax APIs. The AI-assisted transaction reconciliation use case illustrates how an agent-driven orchestration layer could enhance those workflows with structured analysis and guided resolution.

What AI-assisted transaction reconciliation does

In this use case, AI-assisted transaction reconciliation could:

• Compare ERP transaction data with AvaTax records

• Detect missing transactions and mismatched values

• Identify status inconsistencies

• Analyze root causes of discrepancies

• Recommend appropriate resolution actions

• Guide users through review and approval before changes are applied

This workflow illustrates how manual reconciliation processes could be transformed into a structured, guided experience.

Business value

AI-assisted transaction reconciliation is intended to support the following outcomes:

• Accelerated month-end close

  Reduce reconciliation time by identifying and resolving discrepancies efficiently.

• Audit readiness

  Maintain documented transaction records with traceable resolution actions.

• Proactive issue detection

  Detect discrepancies before they affect tax returns.

• Reduced manual effort

  Identify root causes and recommend specific corrective actions.

• Compliance confidence

  Ensure AvaTax transactions align with source systems before filing.

Key assumptions

AI-assisted reconciliation operates under the following assumptions:

• ERP transaction data is accessible

• Users have appropriate permissions

• Multicompany or entity mapping is defined

• Review and approval are required before modification

Resolution actions would require explicit user confirmation. No automatic correction would occur without consent.

Important

The workflows and scenarios described in this document represent forward-looking, agentic AI use cases. They illustrate how AI-driven transaction reconciliation experiences could operate using Avalara integrations and APIs.

These examples are conceptual and demonstrate potential capabilities. They don’t represent fully deployed agents or product functionality unless explicitly stated. Implementation details, behavior, and availability change over time. AI-generated analysis and recommendations should be reviewed before action is taken, as results depend on system configuration, integration behavior, and data quality.