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Document Classification & Entity Extraction

Banking & Financial Services
case study

Business Impacts

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.

Challenges

 

  • 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

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.

Solution

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

Result

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

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