What challenges did Onity Group face before AI implementation?

Process millions of pages across hundreds of mortgage document types with dense legal text, inconsistent handwritten entries, and notarization/seal verification needs. Traditional OCR and AI/ML solutions struggled with accuracy and cost.

Source

Evidence: Low35/100

Automating complex document processing: How Onity Group built an intelligent solution using Amazon Bedrock

Use case typeIntelligent document processingUpdated Jun 13, 2026

Onity Group, through PHH Mortgage Corporation and Liberty Reverse Mortgage, processes millions of pages across hundreds of mortgage document types each year. The company built an intelligent document processing workflow that uses Amazon Textract for text extraction and Amazon Bedrock foundation models for complex visual and contextual analysis, including notarization verification, rider extraction, appraisal checklist validation, and credit report parsing. Documents are uploaded to Amazon S3 and routed through custom classification and extraction logic that chooses between Textract and Bedrock based on document complexity, with security controls using AWS KMS and AWS IAM.

Organization: Onity Group
Industry: Finance
Location: United States
Published: May 2025

Reported outcomes

Accuracy: More than 65% higher

Quality & accuracy

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Cost: 50% decrease

AWS Machine Learning BlogMay 20, 2025Blog postInferred claimLow evidence strength

Reported 50% reduction in document extraction costs.

Normalized claim

Accuracy: 20% increase

AWS Machine Learning BlogMay 20, 2025Blog postInferred claimLow evidence strength

Reported 20% improvement in overall accuracy versus the previous OCR and AI/ML solution.

Normalized claim

Accuracy: 85%

AWS Machine Learning BlogMay 20, 2025Blog postInferred claimLow evidence strength

Credit report processing achieved accuracy up to 85%.

Normalized claim

Accuracy: 65% increase

AWS Machine Learning BlogMay 20, 2025Blog postInferred claimLow evidence strength

Appraisal checklist review improved accuracy by 65% over manual review.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Onity Group
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Document Processing
2Intelligent Automation
3OCR

Built an intelligent document processing workflow that uploads documents to Amazon S3, extracts content with Amazon Textract, classifies documents with a custom AI model, and dynamically routes extraction tasks between Textract and Amazon Bedrock foundation models based on content complexity.
Used Bedrock text and vision models for tasks requiring contextual or visual understanding, such as notarization verification and complex form parsing.
Stored extracted information in structured formats for downstream processing.

Technologies

Amazon Bedrock Amazon Textract Amazon S3 AWS KMS AWS IAM

Appraisal checklist review improved accuracy by 65% over manual review.

Architecture

Documents are uploaded to Amazon S3, preprocessed, extracted with Amazon Textract, classified with a custom AI model, and then dynamically routed to Amazon Textract or Amazon Bedrock text/vision foundation models depending on the document type and extraction complexity. Output is stored in operational databases and Amazon S3. Security controls include AWS KMS and AWS IAM.

Sources & evidence1

Evidence: Low35/100Evidence strength