GL Coding for Accruals
The Hyperbots Accruals Co-pilot's AI-driven GL recommender automates GL coding using historical data, vendor logic, item intelligence, and self-learning from human corrections.
Key Features
AI-driven GL recommender
The GL recommender automatically assigns GL codes based on historical postings for accruals, expense categories, items, and other relevant factors, eliminating manual effort.
Historical data learning
At setup, the system analyzes historical accruals and GL posting data to establish accurate coding patterns for future recommendations.
Vendor and expense-based logic
GL codes are suggested based on accruals history and specific expense categories, ensuring consistency and compliance with accounting policies.
Item and category intelligence
The system incorporates item-level details and broader categories to recommend GL codes for line-item-based accruals, if applicable.
Self-learning from human corrections
The system learns from human corrections during the approval process, reinforcing its accuracy over time.
Context-aware recommendations
The AI considers additional factors such as payment terms, invoice descriptions, and PO details to refine GL code suggestions.
Adaptive and scalable
The GL recommender evolves with changing organizational needs, learning from new vendors, categories, and updated business rules.
Transparency and human validation
Recommendations are presented with explanations in the co-pilot UI, allowing users to validate or override the suggested GL codes.
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: GL Coding for Accruals
How does the Hyperbots Accruals Co-Pilot recommend GL codes for accruals?
The Co-Pilot uses an AI-driven GL recommender that analyzes historical postings, vendor details, and expense categories to automatically suggest accurate GL codes for accruals.
Can the GL recommender learn from corrections made by users?
Yes, the system incorporates human corrections during the approval process to refine its recommendations over time, improving accuracy and alignment with organizational policies.
Does the Co-Pilot consider additional factors like item details or invoice descriptions for GL coding?
Absolutely. The AI evaluates context, including payment terms, item categories, and invoice descriptions, to provide precise and context-aware GL code suggestions.
How does the Co-Pilot ensure transparency in its GL code recommendations?
The recommendations are presented with explanations in the Co-Pilot UI, allowing users to review, validate, or override the suggested GL codes as needed.
Is the GL recommender adaptable to changes in organizational needs?
Yes, the system evolves with changing vendors, new categories, and updated business rules, ensuring scalability and adaptability for long-term financial operations.
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!