Accruals for Pending Invoices
At the accruals cut-off, the Accruals Co-Pilot queries the Invoice Processing Co-Pilot to identify all pending invoices and dynamically adjusts the accrual list as new information emerges.
Key Features
Data synchronization at cut-off
On the accruals cut-off date, the Hyperbots Accruals Co-Pilot initiates a query to the Hyperbots Invoice Processing Co-Pilot for a real-time list of all invoices that have been received but remain unposted or pending.
Status and criteria check
The Invoice Processing Co-Pilot returns invoice details—such as vendor name, invoice amount, due date, and service delivery period—enabling the Accruals Co-Pilot to identify which invoices meet the criteria for accrual (e.g., services rendered or goods received prior to the cut-off, but invoice not yet recorded).
Accrual candidate listing
Using the returned information, the Accruals Co-Pilot compiles a dynamic list of pending invoices that are eligible for accrual. This ensures that any invoice not fully processed by the accounting system is ready to be accrued, preventing understatement of liabilities.
Dynamic adjustment of accrual window
If the accruals window is longer or if certain invoices remain unbooked after the initial cut-off sweep, the Accruals Co-Pilot adjusts its filters and time horizons. This iterative process ensures newly identified pending invoices are added to the accrual list before the final close.
Continuous dialogue between co-pilots
Throughout the accrual closing process, the Accruals Co-Pilot continues to “speak” to the Invoice Processing Co-Pilot, refreshing the list as invoices get posted or new pending invoices surface. This ensures the accrual list remains current and accurate up to the actual close date.
Controlled visibility and reporting
Finally, the Accruals Co-Pilot presents the finance team with a clear, up-to-date accrual register that includes all pending invoices identified. Any changes—such as additional invoices discovered or previously listed invoices posted—are reflected in near-real time, improving the accuracy and transparency of the closing process.
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
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 Pending Invoices
How does the Hyperbots Accruals Co-Pilot initially identify which invoices are pending at the accrual cut-off date?
The Accruals Co-Pilot queries the Invoice Processing Co-Pilot at the cut-off date, retrieving a real-time list of all received but unposted invoices. This provides an immediate snapshot of what is pending and potentially requires accrual.
What role does the Invoice Processing Co-Pilot’s returned data (e.g., vendor, amount, due date) play in determining whether an invoice qualifies for accrual?
The returned invoice details help the Accruals Co-Pilot assess if the expense relates to services rendered or goods received in the current period but not yet invoiced. Criteria like delivery dates and service periods ensure that only those invoices truly representing incurred expenses are accrued.
In what ways does the Accruals Co-Pilot ensure that invoices identified for accrual remain accurate if the accrual window extends beyond the initial cut-off?
By dynamically adjusting its filters and considering a longer accrual window, the Accruals Co-Pilot continuously updates the list as new pending invoices surface or get posted. This iterative process captures any late-arriving information before the final close.
How do ongoing communications between the Accruals Co-Pilot and the Invoice Processing Co-Pilot help maintain an up-to-date accrual list throughout the closing process?
The two Co-Pilots remain in constant dialogue. As invoices are posted or new pending invoices appear, the Accruals Co-Pilot refreshes its data. This ensures the accrual list is always current, even as circumstances change near period-end.
What happens to the accrual list when previously pending invoices are finally posted after the initial cut-off query?
Once a pending invoice is posted, it is automatically removed from the accrual list. This real-time adjustment prevents double-counting and ensures that the liabilities recorded as accruals accurately reflect only those invoices still outstanding at the close.
How does the Accruals Co-Pilot’s dynamic filtering and time horizon adjustments prevent liabilities from being understated?
By continuously adding eligible invoices that appear within the adjusted accrual window, the Co-Pilot ensures that no unprocessed invoices slip through. This approach eliminates the risk of understating liabilities by missing invoices that become known after the initial cut-off.
What benefits do finance teams gain from having a near-real-time, controlled visibility of all pending invoices in the accrual register?
Finance teams gain immediate clarity and transparency over their accrued liabilities, enabling more accurate financial statements. This visibility also streamlines the review process, reduces guesswork, and enhances confidence in the closing process’s integrity.
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