Accruals Cut-Off Scenarios
Hyperbots Accruals Co-pilot supports configurable cutoff scenarios, including month-end, start-of-month, weekly, and daily accruals, tailored to business needs.
Key Features
Month-end accruals
Configures accruals to be performed on the last day of the month for accurate monthly reporting.Example: A company accrues expenses for goods received but not invoiced on the 31st of each month.
Start-of-month accruals
Supports accruals at the beginning of the month for transactions from the previous month.Example: A services company accrues the previous month’s invoices on the 1st of the new month.
Weekly accruals
Handles accruals on a specific day of the week for industries with high inventory turnover.Example: A manufacturing firm accrues material costs every Friday for inventory received that week.
Daily accruals
Facilitates daily accruals for companies with rapid turnover of goods or raw materials.Example: A fast-moving consumer goods company accrues daily for raw material shipments.
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.
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!