Sun Finance automates ID extraction and fraud detection with generative AI on AWS
Sun Finance processed 80,000 monthly microloan applications but approximately 60% required manual operator review, primarily due to OCR extraction errors across multiple languages and ID document types. Per-document costs and approximately 3 FTEs dedicated to manual verification blocked expansion into lower-value loan markets, while about 10% of daily requests were fraudulent, requiring time-intensive manual review.
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 · Loan application triggers ID check
A loan application arrives and the ID image is submitted to the extraction pipeline via an AWS Lambda function.
The multi-tier 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. Manual intervention is projected to drop from 60% to 30% of applications, with staffing projected to decrease from approximately 3 FTEs to approximately 1 FTE.
What failed first
Sun Finance's 2019 IDV system hit accuracy limits as the company expanded into regions with languages underrepresented in OCR training data and multiple ID formats. A replacement attempt using Claude Sonnet 4 alone via Amazon Bedrock achieved only 61.8% accuracy—below the existing baseline—because the model's built-in privacy safeguards refused to extract PII from identity documents, causing ID number extraction to fall to only 43%.