Image
IDP Blog

What is Intelligent Document Processing (IDP) & How Kanverse IDP can help?

Enterprises handle a plethora of documents daily. These documents are often manually processed by humans, and then the relevant data is manually entered into the application systems for storage and future retrieval purpose. Relying entirely on human efforts to process the documents and analyze the information leads to longer cycle times, unwanted errors, increase in costs, and hampers productivity.

Kalpesh Saraf from NASSCOM hosted Dr. Akhil Sahai, Chief Product Officer (CPO) Kanverse.ai. He shares his experience in resolving document processing challenges for global enterprises and creating faster ROI realization routes.

What is Intelligent Document Processing (IDP)?

The idea about Intelligent Document Processing (IDP) is to digitize the entire document processing workflow across business processes by eliminating touchpoints that require manual intervention.

Kanverse IDP product digitizes document processing for enterprises. It intelligently classifies, captures, and extracts all data from documents entering the workflow. It then organizes the information based on business need. The raw data is sanitized and cleansed by the system during processing.

Once the data has been validated and verified, the system automatically exports it to downstream business applications. The entire process is powered by Artificial Intelligence (AI) and advanced machine learning (ML) algorithms to make business processes more resilient to disruptions and help mitigate risks.

Document processing powered by Cognitive Automation

Kanverse IDP follows a cognitive automation approach to document processing. The cognitive automation approach overcomes rule-based automation limitations and complements it with AI-based technologies to achieve automation excellence!

Characteristics of cognitive automation

  • Mimics human operators throughout the workflow – vision, language & pattern detection
  • Can process complex judgment-oriented tasks
  • Learns or improve its performance over time
  • Provide probabilistic output in case of judgment-oriented processes

Differentiators from traditional automation

  • Use of AI technologies like Machine Learning (ML), Natural Language Processing (NLP), and Fuzzy Logic to process documents
  • Influential and intuitive advanced analytics capability
  • Handling unstructured and semi-structured data and converts it into structured data

Factors that are driving cognitive automation adoption

  • Faster processing of semi-structured and unstructured data
  • Improves customer and employee experience – Supports topline growth
  • Improves overall process accuracy, quality, speed, and workforce productivity

Intelligent Document Processing (IDP) adoption trends by industry

  • BFSI: Leverages IDP solutions to process KYC documents, invoices, insurance claims, bank statements, and checks.
  • Healthcare: Processes documents related to patient onboarding, records, surveys, physician referrals, using IDP solutions.
  • CPG and Retail: Witnessing increased adoption of IDP in areas such as proof of delivery, custom declarations, driver and maintenance logs.
  • Manufacturing players: Process invoices, order forms, change requests, proposals, and quality assurance records using IDP solutions.
  • Travel & logistics: Major use cases witnessed are user documentation, invoice processing, proof of delivery, and purchase orders

Significant challenges with traditional AP Invoice automation approaches

  • Vendors often generate invoices in different form factors – Which leads to high-touch based manual processing.
  • Exception handling is manual driven and inefficient, AP staff members often need to go back to the vendors to fix issues.
  • Approval workflows are sparse in nature.
  • Invoice automation projects frequently only address document digitization and do not take care of validation against residing business data.
  • Checking invoice data is undertaken manually before manually filing them in ERP systems.
  • Process bottlenecks are difficult to identify because of the lack of visibility of the end-to-end processes.
  • Organizations often face bottlenecks to address the root cause of issues

As per Gartner, by 2025, 50% of business-to-business invoices worldwide will be processed and paid without manual intervention and by 2030, 80% of business-to-business invoices worldwide will be transmitted digitally.

Instilling cognitive automation approach – Transforms Finance workflows: It enhances efficiency and process effectiveness

  • 50% - 60% - Optimization in operating cost
  • 60% - 80% - Improvement in process cycle times
  • 60% - 70% - Reduction in invoice processing errors
  • 70% - 80% - Faster data collection and processing

Intelligent Document Processing (IDP) use cases

  • AP Invoice automation - End to end automation from incoming invoices in email/shared folder to automated extraction and filing into downstream systems.
  • PO Processing Automation - Extract relevant data from PO, verify and link with quotation, after approval trigger sales order.
  • Quotation Processing Automation - Process customer quote requests in minutes and with high data accuracy.

Positive Business Impact with Kanverse IDP product

  • AI-Powered document categorization and processing. Multichannel document import.
    • Reduced cycle time, manual errors and optimize operational cost
  • AI-powered document processing propels extraction with higher accuracy
    • Eliminates need to create document extraction templates
    • Achieve high document extraction accuracy rates
  • Domain based pre-trained models. Out of the box prebuilt connectors – Oracle EBS, Fusion, NetSuite.
    • Faster deployment and low maintenance
  • Extracted data validated based on business rules
    • Automatic data verification and publish
  • Analytics and monitoring dashboard
    • Complete visibility into end-to-end processes

Source: Gartner and Everest Group
About the author

Aritro Chatterjee, Product Management, Kanverse.ai

Restricted HTML

  • Allowed HTML tags: <a href hreflang> <em> <strong> <cite> <blockquote cite> <code> <ul type> <ol start type> <li> <dl> <dt> <dd> <h2 id> <h3 id> <h4 id> <h5 id> <h6 id>
  • Lines and paragraphs break automatically.
  • Web page addresses and email addresses turn into links automatically.