Business Impact

  • 97.8%

    Classification accuracy

  • 700+

    Documents in 2 minutes processing speed

Customer Key Facts

  • Location : North America
  • Industry : Financial Services

Problem Context

The client, a leading federal national mortgage association that receives over one million paper documents a year, including invoices, tax statements, and checks from their customers and vendors was compelled to manually sort and organize the documents. This was posing a risk for fraud that could go undetected due to the large volume and scale of these documents.



  • Manual effort to digitize and classify 1+ million documents per year
  • Entity extraction in a template-free format
  • Documents of more than one type might be packaged together or on the same page (i.e. invoices and checks)

Technologies Used

Google Cloud Vision API

Automating the Classification & Digitization of Documents with Document AI

The client wanted to organize their service reimbursement process by automating the digitization of documents and efficiently detecting fraudulent requests.


Quantiphi developed a machine learning-based custom document classification model to organize and extract information from these documents into a structured dataset at scale.


  • Cost optimization
  • Time savings
  • Enterprise grade accuracy levels for Optical Character Recognition

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