Precision in Accruals: Predicting Month-End Liabilities
Moving from "best guess" estimates to data-driven precision for unbilled expenses.
Accruals are the dark matter of the balance sheet—invisible until they hit, and often based on estimates that drift from reality. The traditional process of emailing department heads to ask "what have you spent but not been invoiced for?" is inefficient and inaccurate.
AI transforms accruals from a survey-based estimation to a data-driven calculation. By analyzing purchase orders (POs), goods receipts (GRs), and historical vendor billing patterns, AI agents can predict liabilities with high precision.
1. Confidence Scoring & Thresholds
Not all accruals are created equal. A recurring software subscription is deterministic; legal fees are stochastic. AI agents assign a confidence score to every potential accrual.
For high-confidence items (e.g., a GR exists for a PO with fixed pricing), the agent can auto-post the accrual entry without human intervention. For low-confidence items (e.g., a services contract with variable hours), the agent drafts the entry and routes it to the controller for review, along with the supporting data. This risk-based approach dramatically reduces the manual workload.
2. The GR/IR Clearing Automation
The Goods Received / Invoice Received (GR/IR) account is often a mess of aged items. AI agents constantly reconcile this clearing account. They identify instances where goods were received months ago but no invoice appeared, prompting the AP team to contact the vendor or write off the balance if it falls below a threshold.
Clean GR/IR accounts mean a cleaner P&L and fewer surprises during the annual audit.
3. Analyzing Services via SOWs
Accruing for services (consulting, legal, marketing) is notoriously difficult because there is no "Goods Receipt." AI agents can now ingest Statements of Work (SOWs) and project management data.
By connecting to project tools like Jira or Asana, the agent can correlate project progress with the payment schedule defined in the contract. If a milestone is marked 50% complete in the project tool but no invoice has been received, the agent proposes a 50% accrual, bridging the gap between operations and finance.
4. Seasonality & Utility Adjustments
Simple run-rate matching fails for variable expenses like utilities or cloud computing costs. AI models incorporate seasonality and usage trends into their predictions.
For a manufacturing plant, the agent correlates electricity accruals with production volume data. For cloud costs, it looks at daily usage APIs. This results in accruals that reflect actual activity levels rather than just "last month's bill," preventing painful true-up shocks.
5. The "True-Up" Feedback Loop
The most powerful feature of AI accruals is the feedback loop. When the actual invoice finally arrives, the system compares it to the accrued amount.
If there is a variance, the model learns. If "Vendor A" consistently bills 5% higher than their PO amount for shipping, the model adjusts future accruals for Vendor A by +5%. Over time, this self-correcting mechanism makes the estimator increasingly precise.
6. Autonomous Vendor Outreach
Sometimes the best way to get an accurate accrual is simply to ask. AI agents can autonomously identify missing invoices for high-value POs near month-end and email the vendor directly.
"Dear Vendor, we show PO #1234 as delivered but have not received an invoice. Please submit by Friday to ensure payment in this cycle." This proactive approach often elicits the actual invoice before the books close, eliminating the need for an accrual entirely.
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See how ChatFin improves accrual accuracy.