Industry leaders have successfully imbibed the culture of data-driven business decision-making across all processes. However, the transformative path to data-driven decisions comes with numerous challenges. Unfortunately, 80% of all business data are inaccessible to generate insights, as it resides in silos across multiple locations and in unstructured formats. The data often enters the enterprise from multiple channels in structured, unstructured and semi-structured forms as images, emails, PDFs et al. Tracking the movement of data is a daunting task - without witnessing a substantial increase in costs.
What is AI Invoice processing?
Using an accounts payable automation software powered by multiple Artificial Intelligence (AI) technologies - helps businesses to streamline invoice processing. Advancements in artificial intelligence technologies are helping organizations attain a very high data extraction accuracy from invoices.
Kanverse.ai provides enterprises with up to 99.5% invoice data extraction accuracy – out of the box.
Accounts payable automation software can extract and process data from a variety of invoices. The entire workflow is powered by the culmination of multiple AI technologies that include natural language processing (NLP), deep learning, computer vision, and machine learning (ML). Enterprises can now track, monitor, extract, synthesize and process unstructured invoice data based on business requirements.
Invoice Processing powered by Computer Vision
Combining optical character recognition (OCR) technology with computer vision has substituted the traditional templatized invoice processing approaches. It saves time by eliminating the need to create templates.
Powerful deep learning models ensure data from documents can be extracted from a variety of surfaces and backgrounds. These include – multi-invoice documents, multi-page invoices, and skewed invoices. Vision models can understand and comprehend different areas in the invoice and extract data.
Document Processing using Machine Learning
Accounting departments are the prime target of frauds. Advancements in machine learning can help business leaders to detect and manage risks – which will safeguard their operations. Deploying automation and machine learning to accounts payable cores helps to detect and prevent fraud attempts before they do can do any damage.
Machine Learning models helps in data extraction from documents bearing multiple formats, can help to better understand relationships between documents, validate, and augment information based on business needs.
Benefits of document processing using Machine Learning
- Higher extraction accuracy - ML reduces process cycle time and with higher extraction accuracy. It reduces errors caused by manual entry.
- Faster data processing - A trained ML model can process invoices requiring weeks or months of effort when done manually in a matter of days. It increases process efficiency and productivity.
- Improved employee productivity - Machine learning reduced manual touchpoints by pulling out relevant information from invoices. It enables employees to spend more time on business-critical activities.
- Cost savings - It eliminates all the repetitive and time-consuming activities involved in processing invoices - reducing the average processing cost per document.
Applying NLP for Intelligent Document Processing
The combination of Natural Language Processing (NLP) and Machine Learning (ML) technology is ideal for processing invoices bearing different languages.
What makes NLP an extremely valuable technology to process documents?
Named Entity Recognition - It automatically identifies named entity mentions within documents and classifies them based on pre-defined requirements. It automatically locates all the entities associated with the supplier and maps corresponding invoice line-item details based on existing records.
Text Classification - NLP text classifiers can automatically analyze the extracted text from invoice and then assign a set of pre-defined tags. Enterprises can build multiple use cases to detect anomalies and intent from the invoice that enter the workflow. A system of checks and balances can be instated, based on which the administrator can initiate necessary actions.
Fuzzy Logic - Fuzzy Logic can mimic how agents makes real time decisions. Complimenting NLP with fuzzy logic supports decision making, improves system performance and contributes to enhancements in efficiency across the business processes.
About the author
Aritro Chatterjee, Product Management, Kanverse.ai