AWS Machine Learning for document processing and underwriting

Make it easy to process and extract information to process loan applications,
review documents, and analyze financial data.

Explore featured services



  • FinServe_Texttract

    Amazon Textract

    Extract printed text, handwriting, forms, and tables from virtually any document using OCR to eliminate manual processes.

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  • FinServe_SageMaker

    Amazon SageMaker

    Helps you build, train, and deploy ML models for building custom models for text extraction and analysis so you can quickly process print, handwritten, and electronic documents, such as PPP loans.


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  • FinServe_Comprehend

    Amazon Comprehend

    Extracts relevant details, insights, relationships from text and categorizes documents using natural language processing (NLP).

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  • FinServe_AugmentedAI

    Amazon Augmented AI

    Makes it easy to build the workflows required for a human to review the output from any ML or AI service, providing you the human oversight needed for precision. 

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Unlock intelligent document processing

The US Paycheck Protection Program is driving a need for accelerated document processing. The US Paycheck Protection Program is a Small Business Administration (SBA) initiative that helps businesses keep their workforce employed during COVID-19.

The second round of the Paycheck Protection Program (PPP) is designed to help small businesses provide additional relief to their employees and businesses. Similar to the first round, businesses fill out a loan application and provide several supporting financial documents for approval. The application process can be challenging to many lenders due to the volume of applications they receive and the number of pages per submission. With Machine Learning, several AWS customers have automated the loan approval process—reducing approval times from days to hours and accelerating the delivery of much-needed funds to millions of small businesses. Contact us to learn more. »

Using AWS, Pinterest has tripled its use of storage and compute over just two years without worrying about reliability or scalability. Pinterest provides one of the world's largest visual-bookmarking tools, with more than 200 million users and 2 billion boards. The company has used a variety of AWS services to scale its processing, storage, and data-analysis workloads to help developers focus on delighting customers.

Featured use cases for AWS Machine Learning

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FEATURED THOUGHT LEADERSHIP

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This blog discusses how to think about and understand financial services cloud security and AWS services, how to get started with automation in the cloud, and provides self-service cloud security resources.
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Streamlining the Development of Deep Learning Applications in an AI-Enabled World

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NFL eBook: Going Long on Machine Learning

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Textract eBook:
Automate document processing with AI

This blog discusses how to think about and understand financial services cloud security and AWS services, how to get started with automation in the cloud, and provides self-service cloud security resources.
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ML Journey

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Automate document processing

Kabbage is a data and technology company providing small business cash flow solutions, including access to flexible lines of credit, online payments, cash-flow insights and business checking accounts. Read the Kabbage blog for more details.

Read the blog »

BlueVine collaborates with AWS

BlueVine’s online platform provides business owners with fast and easy solutions to everyday financial needs. BlueVine harnessed Amazon Textract to achieve a high degree of automation for document processing, helping thousands of businesses across the US receive funds as part of the PPP program, and saving more than 400,000 jobs. Read the BlueVine blog for more details.

Read the blog »

Read the AWS reference guide »


Information includes:

  • An overview of the Small Business Association’s (SBA) Paycheck Protection Program
  • Customer examples and use cases
  • A practical list of Amazon Machine learning services that can help you process applications more quickly and improve the overall underwriting process


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