Adjusting Accruals Based on Pending Invoices

Queries pending invoices at cut-off and updates the accrual list in real time to support accurate and automated month-end close processes.

Key Features

Data synchronization at cut-off

At the accrual cut-off, the system checks with the invoice processing co-pilot to get a real-time list of received invoices that are still unposted or pending.

Status and criteria check

The invoice co-pilot shares key details like vendor, amount, and delivery period, so the accruals co-pilot can identify which invoices should be accrued, such as those for goods or services delivered before the cut-off but not yet posted.

Using the returned information, the Accruals Co-Pilot compiles a dynamic list of

Accrual candidate listing

Based on the shared invoice details, the accruals co-pilot creates a live list of invoices that should be accrued, helping ensure no pending costs are missed in financial reporting.

Dynamic adjustment of accrual window

If some invoices are delayed or the accrual window is extended, the co-pilot automatically updates its filters and timelines, making sure all relevant invoices are captured before closing.

Continuous dialogue between co-pilots

The Accruals Co-Pilot stays in sync with the Invoice Processing Co-Pilot during the close process, updating the list as new invoices come in or get posted, so the accruals stay accurate right up to final close.

Controlled visibility and reporting

The Accruals Co-Pilot gives finance teams a clear, up-to-date view of all pending invoices in one place. As new invoices are found or existing ones get posted, the register updates automatically, helping teams close with greater accuracy and transparency.

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?

Why choose hyperbots agentic AI: finance-first, accurate, adaptable AI

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.

Finance Transformations Powered by Agentic AI

Real outcomes from finance teams using Hyperbots Agentic AI

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    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Vendor Management | Procurement

    80%

    Straight-Through Processing

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    Extraction Accuracy

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  • $5B+ Miami-based Manufacturing Company

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    85%

    Straight-Through Processing

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    Cost Reduction

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    90%

    Straight-Through Processing

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    Accelerated Inventory Updates

  • Houston-based Petrochemical Marketing Company

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    70%

    Reduction in

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    Reduction in

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  • $1B+ Global Multi-Entity Pharmaceutical Company

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    Hyperbots Agents Used

    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection

    80%

    Straight-Through Processing

    90%

    Reduction in Invoice Processing Time

    Zero

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  • $9B+ Manufacturing Enterprise

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    Hyperbots Agents Used

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    80%

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    Straight-Through Processing

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    80%

    Reduction in

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    80%

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    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Procurement | Vendor Onboarding | Payments

    24 -> 2

    Guest Management

    Steps Reduction

    10 -> 2 Weeks

    Reduction in

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    80%

    Increase in

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    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection

    80%

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    99.5%+

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    STP Achieved in Cash Reconciliation

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    STP Achieved in

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  • San Francisco-based Healthcare Payment Solutions Provider

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    40%

    Close Cycle

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    80%

    STP in

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    Time to Procure

  • $1B+ Miami-based Global Seafood Supplier

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    ERPs
    SAP S/4HANA
    Hyperbots Agents Used

    Accruals | Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Validation | Augmentation

    80%

    Reduction in

    Time to Accrue

    80%

    Improvement in

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  • NYC-based Largest Animal Hospital Operator

    Optimized for high complexity across vendor management, accounts payable, and vendor payments, requiring a modern, highly accurate, and transformative solution to optimize finance operations
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    NetSuite
    Hyperbots Agents Used

    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Procurement | Validation | Augmentation

    60%

    Reduction in

    Vendor Management Overhead

    70%

    Gain in

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  • Boston-based Furniture Manufacturing Firm

    Optimized a complex AP environment with multi-tier supply chains, mixed-vendor purchasing, 3-way matching, freight audits, progress billing, & required a custom ERP integration
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    Hyperbots Agents Used

    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Procurement | Validation | Augmentation

    80%

    Straight-Through Processing

    85%

    Reduction in

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  • NYC-based Design & Fabrication Firm

    Managed multi-currency AP, import-export compliance, taxes, freight, and vendor operations across the U.S., Mexico, and China
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    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Validation | Augmentation | Vendor Management

    80%

    Reduction in Vendor Management Overhead

    100%

    Tax Compliance

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    Accuracy in

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  • Texas-based Non-Profit Organization

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    Email Triaging | Extraction | Invoice Processing | Matching | Fraud Detection | Validation | Augmentation | Vendor Management

    80%

    Reduction in

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    AI Extraction Accuracy

  • Chicago-based Energy R&D Organization

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    70%

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