Agent StoreBillingBilling Reconciliation
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Billing Data Reconciliation Agent

BillingBilling Reconciliation

Cross-checks billing system records against the general ledger, payment processor, and subscription database every day to catch mismatches before month-end close.

4
Process steps
5
Integrations
3
Data inputs

Billing data lives across the subscription platform, the payment processor, and the general ledger, and today these systems drift apart from sync failures, manual journal entries, or timing differences, surfacing only when accounting can't tie out cash to invoiced revenue at month-end close, often forcing a scramble across multiple teams

Waiting until close to discover a discrepancy means the root cause may be weeks old and hard to trace

This agent runs a daily three-way reconciliation between the billing system, the payment processor's settlement records, and the general ledger, matching transactions by invoice ID and flagging any mismatch in amount, status, or timing the same day it occurs

It classifies each mismatch by likely cause, whether a sync failure, a manual override, or a timing difference that will self-resolve, and routes only the genuine discrepancies to accounting

The agent pulls daily transaction extracts from the billing system, the payment processor's settlement report, and the general ledger's accounts receivable subledger, then matches records on invoice or transaction ID across all three sources. Discrepancies in amount, status, or date are classified using a rules engine trained on historical resolution patterns, self-resolving timing differences are logged and suppressed from alerts, and genuine mismatches are packaged with supporting detail and routed to the accounting team's reconciliation queue.

1

Extract Daily Transaction Data

  • Pull the day's invoices and payments from the billing system
  • Pull settlement records from the payment processor
  • Pull corresponding AR subledger entries from the general ledger
  • Normalize transaction identifiers across all three sources
Outcome: A complete daily dataset from all three systems is ready for matching.
2

Match and Compare

  • Match transactions across systems by invoice or payment ID
  • Compare amount, status, and date fields for consistency
  • Identify unmatched records present in one system but not others
  • Calculate the total dollar value of all discrepancies found
Outcome: Every discrepancy between the three systems is identified the same day it occurs.
3

Classify Discrepancies

  • Classify each mismatch as timing difference, sync failure, or manual override
  • Suppress alerts for timing differences expected to self-resolve within 48 hours
  • Flag sync failures for engineering review
  • Escalate manual override discrepancies to accounting with full detail
Outcome: Only genuine, actionable discrepancies reach the accounting team.
4

Report and Track Resolution

  • Publish the daily reconciliation summary with variance totals
  • Track open discrepancies until resolved and confirm closure
  • Maintain a rolling history of recurring mismatch sources
  • Provide a clean pre-reconciled dataset for month-end close
Outcome: Month-end close starts from an already-reconciled position instead of a backlog of unknowns.
Stripe
sources payment settlement records
NetSuite
provides general ledger AR subledger data
Chargebee
sources billing system invoice and payment status
Jira
creates engineering tickets for sync failures
Slack
alerts accounting of new manual-override discrepancies