Collect or forward your emailed balance sheet to your Nanonets Inbox.
Snap a picture and Nanonets will take care of the rest.
Accurately capture predefined labels with Artificial Intelligence. Reconcile data across sources.
Automate Asset management workflows/processes and more.
Automate Financial planning and analysis workflows/processes and more.
Automate Financial reporting workflows/processes and more.
Here are some fields Nanonets can extract by default. Say goodbye to manual data entry. Additional fields can also be extracted on request.
Learn more about balance sheet automation & how Nanonets can help.
Automated data extraction for Balance Sheets uses Intelligent Document Processing (IDP) platforms to automatically identify, capture, and extract specific financial information from these critical documents. This goes beyond traditional OCR by employing advanced AI and machine learning to understand various balance sheet formats' context, layout, and structure.
This means key financial figures such as assets (current, fixed, intangible), liabilities (current, long-term), and equity (share capital, retained earnings) are accurately identified and pulled, whether from scanned PDFs, digital files, or even images. The system then converts complex, unstructured, or semi-structured data into structured, actionable data (e.g., CSV, JSON, integration with accounting software), eliminating manual data entry, reducing human errors, and preparing information for financial analysis and reporting.
AI and Machine Learning (ML) fundamentally improve the accuracy of Balance Sheet OCR by enabling systems to learn and adapt, overcoming the limitations of traditional, template-dependent OCR. Instead of rigid rules, AI models are trained on vast datasets of financial documents. This allows them to:
Solutions like IDPs are specifically designed to handle various Balance Sheet layouts originating from different accounting systems or formats. Unlike older, template-based OCR systems that require a new template for every unique document design, IDPs leverage advanced AI and machine learning that learn to understand the underlying structure and semantic meaning of financial data. This means the system can:
Automated solutions like Nanonets can accurately extract a comprehensive range of data fields from Balance Sheets, transforming unstructured financial documents into structured, usable data. This granular extraction capability is vital for financial analysis and reporting.
Key data fields typically extracted include:
Beyond these standard line items, advanced platforms like Nanonets can also be trained to extract complex notes, sub-accounts, and specific financial ratios or other custom fields relevant to your organization's unique analysis requirements. The ability to extract structured table data and unstructured text from notes provides a complete financial picture.
Automating Balance Sheet data extraction with platforms like Nanonets offers transformative benefits for finance teams and organizations:
Automated Balance Sheet data extraction, leveraging solutions like Nanonets, has wide-ranging applications across various departments within an organization, extending beyond just the finance function:
Yes, automated data extraction from Balance Sheets, particularly with Intelligent Document Processing (IDP) platforms like Nanonets, significantly supports and streamlines the automated consolidation of financial statements. This is a critical application for multi-entity organizations, holding companies, or enterprises with numerous subsidiaries needing a single, unified financial report.
Balance Sheet automation tools are designed to seamlessly integrate existing ERP systems (e.g., SAP, Oracle, Microsoft Dynamics) and accounting software (e.g., QuickBooks, Xero, Sage, NetSuite). This integration is crucial for creating end-to-end automated financial workflows and ensuring that extracted data is immediately usable. Integration typically occurs through several methods:
This seamless integration ensures that once balance sheet data is extracted and validated by Nanonets, it flows directly into your financial systems, eliminating manual data entry and improving data accuracy and timeliness.
Automating Balance Sheet data extraction, while highly beneficial, presents several common challenges that advanced IDP solutions like Nanonets are designed to overcome:
Nanonets addresses these challenges through its adaptive AI models, which are trained to understand complex financial semantics and layouts, provide intelligent validation, and offer a seamless human-in-the-loop interface for efficient exception handling.
Automated solutions, particularly Intelligent Document Processing (IDP) platforms like Nanonets, employ advanced AI and machine learning, including Natural Language Processing (NLP), to effectively handle unstructured data and complex notes within Balance Sheets. This capability goes significantly beyond what traditional OCR tools can achieve.
Here's how they handle it:
By integrating NLP with advanced OCR and machine learning, Nanonets transforms even the most challenging unstructured data in balance sheets into actionable insights, providing a more complete and accurate financial picture.