Onity Group achieves 50% cost reduction and 20% accuracy improvement in mortgage document processing with Amazon Bedrock
Onity processes millions of mortgage servicing pages annually across hundreds of document types, but traditional OCR and AI/ML solutions could not reliably handle verbose legal text, inconsistent handwritten entries, notarization and legal seal detection, or limited contextual understanding.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Document upload to S3
Documents are uploaded to Amazon S3, which triggers automated processing workflows.
Tools used
Amazon BedrockAmazon TextractAnthropic's Claude SonnetAWS KMS
Outcome
Onity achieved a 50% reduction in document extraction costs and a 20% accuracy improvement compared to their previous OCR and AI/ML solution. Home appraisal checklist review accuracy improved by 65% over the manual process, and credit report analysis achieved approximately 85% accuracy.
What failed first
Traditional OCR and ML models proved fundamentally limited for mortgage servicing documents, failing on four core challenges: verbose legal text with buried data elements, inconsistent handwriting style variations, notarization and seal detection requiring visual understanding, and lack of semantic context interpretation.