Sun Finance automates ID extraction and fraud detection with generative AI on AWS
A case study on using OCR, vision models, and an LLM to automate identity verification and fraud checks. The main value is the architecture pattern: combining specialized extraction with generative structuring can outperform either approach alone.
From the source
In this post, we show how Sun Finance used Amazon Bedrock, Amazon Textract, and Amazon Rekognition to build an AI-powered identity verification (IDV) pipeline. The solution improved extraction accuracy from 79.7% to 90.8%, cut per-document costs by 91%, and reduced processing time from up to 20 hours to under 5 seconds. You'll learn how combining specialized OCR with large language model (LLM) structuring outperformed using either tool alone. You'll also learn how to architect a serverless…
Tags
document-aiidentity-verificationbedrock
Editorial scoring · case_study
Novelty
3.0
Impact
5.0
Technical depth
5.0
Relevance
7.0
Non-promotional
2.0