Forecasting Non-PO Accruals with Agentic AI
Uses time series trends, historical data, and external sources to automate accruals for recurring expenses like utilities.
Key Features
Historical data analysis
Tracks completed service milestones using vendor-provided reports or certificates to identify services rendered but not yet invoiced.
Predictive modeling and time series forecasting
Learns from past usage patterns, seasonal trends, and external factors, like weather, to estimate future expenses and create timely accruals, even before invoices arrive.
Dynamic estimation with external data inputs
Uses both internal records and external data, like energy prices, weather patterns, or market rates, to create more accurate and context-aware accrual estimates.
Agentic platform capabilities
Signals like updated tariffs, rate changes, or usage spikes are automatically factored into accrual estimates, making the process more responsive and less reliant on fixed rules.
Adjustment based on organizational changes
When operations change, like opening a new plant or adding high-energy equipment, the system adjusts its estimates to reflect updated usage patterns, ensuring accruals stay accurate and relevant.
KEY BENEFITS
Accruals Co-Pilot automates detection, posting, and reversal, wiping out month-end busywork; machine learning sharpens forecasts and audit trails; policy-aware configuration snaps into any ERP—cutting errors, risk, and workload in one stroke.
80%
Accrual processing cost
Co-pilot reports all accrued expenses using AI eliminating the need for manual accruals completely
<5%
Variance in accured Vs actual costs
Co-pilot identifies all expenses comprehensively for all type of scenarios through data using AI.
Human Errors
Accrual reversal
Month end closing pressure
Auditability
Why Hyperbots Agentic AI Platform?
Finance specific
Hyperbots Agentic AI platform specializes exclusively in finance and accounting intelligence, leveraging millions of data points from invoices, statements, contracts, and other financial documents. No other platform has such large pretrained models on F&A data.
Best-in-class accuracy
Hyperbots achieves 99.8% accuracy in converting unstructured data to structured fields through a multimodal MOE model integrating LLMs, VLMs, and layout models. With contextual validation and augmentations, the platform ensures 100% accuracy for deployed agents.
Synthesis of unstructured and strutured finance data
Hyperbots agents emulate finance professionals to autonomously perform F&A tasks by reading and writing data like COA, expenses, and vendor masters from core accounting systems and integrating it with unstructured data from financial documents such as invoices, POs, and contracts.
Pre-trained agents with state of the art models
Hyperbots' Agentic platform, pre-trained on millions of financial documents like invoices, bills, statements, and contracts, ensures seamless integration, high accuracy, and adaptability to any accounting content, form, layout, or size from day one.
Company specific inference time learning
Hyperbots' Agentic platform employs state-of-the-art Auto ML pipelines with techniques like reinforcement learning to enable inference-time learning for tasks such as GL recommendation and cash outflow forecasting, ensuring continuous improvement and adaptability.
FAQs: Accruals for Recurring Expenses
How does Hyperbots Co-Pilot predict accruals for recurring expenses without POs or GRNs?
The Co-Pilot uses historical invoice data, time series forecasting, and external factors—like weather or commodity prices—to model expense patterns, enabling accurate accruals even in the absence of formal purchase orders or receipts.
Can the Co-Pilot adjust accrual predictions if there are sudden organizational changes?
Yes. The Co-Pilot factors in new operational parameters (e.g., new facilities opening or increased production lines) to update its baseline estimates, ensuring accruals remain aligned with the actual resource consumption.
What kinds of external data can the Co-Pilot incorporate for more accurate forecasts?
The system can integrate data such as market rate changes, utility tariff updates, weather patterns, and other environmental indicators that influence recurring expenses, helping it refine its predictions.
How does the Agentic AI capability improve the accuracy of these accrual forecasts?
Agentic AI continuously monitors for relevant signals—such as announced rate hikes or abnormal usage patterns—and automatically updates the accrual model in real-time, making the forecasts more adaptive and less reliant on static assumptions.
Can the Co-Pilot handle different time horizons for predicting recurring expenses?
Yes. It can forecast both short-term and long-term accrual needs, adapting its models to monthly, quarterly, or annual cycles depending on the company’s reporting and budgeting requirements.
Is there a mechanism to review and approve these AI-driven accrual estimates before they are posted?
Absolutely. Organizations can implement approval workflows where finance managers or designated approvers review, validate, and if necessary, adjust the AI-generated accrual amounts before they enter the ERP.
What happens if the actual invoice differs from the predicted accrual estimate?
Once the invoice arrives, the Co-Pilot compares it against the accrued amount. If there’s a variance, it automatically adjusts and reconciles the difference, ensuring the financials remain accurate and up-to-date.
