"Automated General Ledger reconciliation that identifies discrepancies between GL and subledger data"
Finance teams spend significant time manually reconciling General Ledger entries against subledger systems. This project automates the reconciliation process, flagging exceptions that require investigation while providing detailed analytics on discrepancy patterns.
The system handles thousands of journal entries with sub-second processing, automatically categorizing discrepancies into actionable exception types for audit compliance and efficient month-end close procedures.
The reconciliation engine automatically categorizes discrepancies into five distinct exception types for targeted investigation:
Creates realistic journal entries with 17 account types across Asset, Liability, Equity, Revenue, and Expense categories with intentional discrepancies for testing.
Processes 10,000+ entries per second with configurable tolerance thresholds for matching precision based on business requirements.
Real-time KPI metrics, status distribution charts, variance analysis by account, and aging breakdowns with drill-down exception tables.
Download exceptions as CSV for audit documentation. Integrates with existing finance workflows for seamless month-end close.
Designed and built the complete reconciliation system from scratch, including the matching algorithm, exception categorization logic, and interactive dashboard.
Applied accounting knowledge to implement proper debit/credit validation, journal entry balancing, and GL-to-subledger matching logic.
Optimized processing to handle 10K+ entries per second using vectorized Pandas operations and efficient data structures.