Accruals for Recurring Expenses
Hyperbots Accruals Co-Pilots leverage historical data, time series forecasting, external inputs, and agentic Al capabilities to predict non-PO expenses like utilities.
Key Features
Historical data analysis
For expenses like utilities, a company’s accounting system maintains a history of monthly or periodic invoices. Even without a PO or GRN, these historical bills serve as a baseline. By analyzing multiple prior periods, patterns emerge, helping to predict expected costs before the invoice arrives.
Predictive modeling and time series forecasting
Using advanced AI techniques, Hyperbots Accruals Co-Pilots can train models on historical consumption patterns, seasonality, trends, and external factors (such as weather data for heating costs). The system can then forecast future costs based on these patterns, allowing for an accrual entry even before the invoice is received.
Dynamic estimation with external data inputs
Beyond historical internal data, the tool can incorporate external datasets—such as commodity prices for energy, rainfall or temperature data (affecting water and heating bills), or general market rate changes. This holistic approach enhances the accuracy of the accrual estimate.
Agentic platform capabilities
The “agentic” nature of platforms like Hyperbots means they actively seek out relevant signals (such as known tariff changes, recently announced rate hikes by utilities, or usage spikes detected by IoT sensors) and incorporate these signals into their accrual estimates. This reduces reliance on static models and makes the process more adaptive.
Adjustment based on organizational changes
If the company opens a new plant, closes a warehouse, or adds energy-intensive equipment, the AI model can integrate these operational changes. By adjusting baseline consumption patterns, the system recalculates the likely next invoice, providing a more realistic accrual figure.
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
VALUE PROPOSITION
Why Hyperbots Accruals Co-Pilot
Hyperbots Accruals Co-pilot automates accrual identification, booking, and reversal processes with high configurability and accuracy, ensuring timely and compliant financial reporting while reducing manual effort and errors.
Why Hyperbots Agentic AI Platform?
Finance specific
Best-in-class accuracy
Synthesis of unstructured and strutured finance data
Pre-trained agents with state of the art models
Company specific inference time learning
FAQs: Accruals for Recurring Expenses
How does Hyperbots Co-Pilot predict accruals for recurring expenses without POs or GRNs?
Can the Co-Pilot adjust accrual predictions if there are sudden organizational changes?
What kinds of external data can the Co-Pilot incorporate for more accurate forecasts?
How does the Agentic AI capability improve the accuracy of these accrual forecasts?
Can the Co-Pilot handle different time horizons for predicting recurring expenses?
Is there a mechanism to review and approve these AI-driven accrual estimates before they are posted?
What happens if the actual invoice differs from the predicted accrual estimate?
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!