AI Accounting Reconciliation

Overview

A multi-entity company with transactions flowing through 8 bank accounts, 3 payment processors, and 2 ERP systems was spending 12 days on month-end close. The accounting team manually matched thousands of transactions, hunted for discrepancies, and documented everything for audit. We built an AI reconciliation engine that automates the matching, learns from corrections, and produces audit-ready documentation.

The Challenge

Transaction matching across systems isn't straightforward — amounts may be split, timing differs between bank posting and ERP recording, descriptions don't match, and currency conversions add complexity. The AI needed to handle fuzzy matching while maintaining the precision required for financial reporting.

Our Approach

We built a multi-pass matching engine. The first pass handles exact matches (same amount, date, reference). The second pass uses AI to identify probable matches — split transactions, timing differences, and description variations. The third pass flags genuine discrepancies. Each month, the AI learns from human corrections on the flagged items, continuously improving match rates.

Key Features

• Multi-system transaction ingestion
• Three-pass matching (exact, fuzzy, AI)
• Split transaction detection
• Currency conversion handling
• Discrepancy flagging with context
• Audit-ready documentation generation
• Continuous learning from corrections

Results

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