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
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: GL Coding for Accruals
How does the Hyperbots Accruals Co-Pilot recommend GL codes for accruals?
Can the GL recommender learn from corrections made by users?
Does the Co-Pilot consider additional factors like item details or invoice descriptions for GL coding?
How does the Co-Pilot ensure transparency in its GL code recommendations?
Is the GL recommender adaptable to changes in organizational needs?
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!