Sun Finance automates ID extraction and fraud detection with generative AI on AWS

Artificial Intelligence Sun Finance automates ID extraction and fraud detection with generative AI on AWS This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, Sergei Kiriasov from Sun Finance Group If you process identity documents at scale—loan applications, account openings, compliance checks—you’ve likely hit the same wall: traditional optical character recognition (OCR) gets you partway there, but extraction errors still push a large share of applications into manual review queues. Add fraud detection to the mix, and the manual workload compounds. Sun Finance, a Latvian fintech founded in 2017, operates as a technology-first online lending marketplace across nine countries. The company processes a new loan request every 0.63 seconds and delivers more than 4 million evaluations monthly. In one of their highest-volume industries, with 80,000 monthly applications for microloans, approximately 60% of applications required manual operator review. Sun Finance partnered with the AWS Generative AI Innovation Center to rebuild the pipeline. Within 35 business days of handover, the solution was live in production. The following timeline shows the full project journey from kickoff to production launch. Sun Finance project timeline from kickoff to production The project moved through four milestones over 107 business days. The AWS Generative AI Innovation Center engagement ran 32 days from kickoff (August 26, 2025) to final presentation (October 9, 2025), followed by 26 days for technical handover (November 14, 2025). Sun Finance then took 35 business days to move the solution into production, including a 14-day production freeze over the holiday period (December 18 – January 7), and went live on January 22, 2026. 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 fraud detection system using vector similarity search. The Identity Verification Challenge Sun Finance had built its first IDV automation in 2019 using Amazon Rekognition and Amazon Textract. As the company expanded into developing regions, the system’s limitations became hard to ignore. This region presented unique challenges with language and document complexity. Processing documents in both English and a local language proved difficult for traditional OCR systems. The local language text remains underrepresented in traditional OCR training datasets, causing frequent extraction errors. Sun Finance also needed to handle 7 different ID types, each with different layouts and formats. The manual workload was primarily driven by OCR errors. Of the 60% of applications requiring manual review, approximately 80% of cases stemmed from mismatches between extracted information and customer-entered data. Critically, 60% of these mismatches were OCR errors, not customer mistakes. The remaining 20% of manual interventions related to fraud detection flags. Fraud detection added another layer of complexity. About 10% of daily requests were actual fraudulent applications.…