Transforming Identity Verification and Fraud Detection at Sun Finance with AWS
A Collaborative Innovation Journey with the AWS Generative AI Innovation Center
Overcoming Challenges in Identity Document Processing
Building a Robust AI-Powered Solution: Key Components and Architecture
Enhancing ID Extraction and Fraud Detection: Detailed Methodologies
Measurable Results: Improved Accuracy, Cost Efficiency, and Operational Impact
Future Directions: Scaling and Enhancing Fraud Detection Capabilities
Key Takeaways and Lessons Learned from the Implementation
Next Steps: Expanding and Improving the AI-Driven Solutions
Conclusion: Delivering Fast and Efficient Service to Microloan Applicants
Revolutionizing Identity Verification in Fintech: How Sun Finance Transformed Document Processing
This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, and Sergei Kiriasov from Sun Finance Group.
In the fast-paced world of fintech, processing identity documents effectively is a cornerstone of operational efficiency. For organizations handling vast numbers of loan applications, account openings, and compliance checks, traditional optical character recognition (OCR) often presents significant challenges. While OCR is a useful tool, it frequently leads to extraction errors, thereby forcing many applications back into the realm of manual review—a labor-intensive, costly process that hinders scalability and efficiency.
Enter Sun Finance, a Latvian fintech founded in 2017 that has established itself as a technology-driven leader in online lending across nine countries. Faced with approximately 80,000 monthly applications for microloans—60% of which required manual review—Sun Finance partnered with the AWS Generative AI Innovation Center to redesign their identity verification pipeline, setting a new standard for efficiency. This collaboration yielded remarkable results, including a drastic reduction in processing costs and an impressive increase in accuracy.
The Project Timeline: From Kickoff to Launch
The project unfolded over 107 business days, culminating in a production launch in January 2026. Key milestones included:
- Proof-of-Concept: From August 26 to October 9, 2025, the solution was fleshed out over 32 days.
- Technical Handover: A 26-day process followed on November 14, 2025.
- Production Launch: Following a 14-day holiday freeze, the solution went live on January 22, 2026.
The Identity Verification Challenge
Initially, Sun Finance relied on its first ID verification automation built using Amazon Rekognition and Amazon Textract. However, as the company expanded, the limitations of this system became evident. The local complexities of language and document formats contributed to a high error rate in extraction, causing a significant backlog in manual reviews.
Analysis revealed that 60% of manual interventions stemmed from mismatches due to OCR errors—a staggering demonstration of the insufficient capabilities of traditional OCR systems. Coupled with the need for robust fraud detection mechanisms, the manual workload became increasingly untenable.
Enter AI-Powered Solutions
The ambitious initiative with AWS led to two critical innovations: an advanced ID extraction system and a parallel fraud detection methodology. The innovative design utilized various AWS services, including:
- Amazon Bedrock: For AI structuring
- Amazon Textract: For OCR text extraction
- Amazon Rekognition: As a fallback OCR system and for face detection
- Amazon S3 Vectors: For serverless vector similarity searches
This synergy of technologies enabled Sun Finance to transform its document processing landscape significantly.
Achieving New Heights in Performance
By utilizing a specialized approach, Sun Finance experienced outstanding improvements:
- Extraction Accuracy: Jumped from 79.7% to 90.8%
- Cost Reduction: Achieved a staggering 91% decrease in per-document costs
- Processing Time: Reduced from 20 hours to under 5 seconds per document
This innovative architecture combined specialized OCR capabilities with large language model (LLM) structuring, demonstrating a superior method over utilizing traditional systems alone.
Lessons Learned
The project yielded five essential insights:
- Integration of Technologies: Utilizing OCR and LLM together outperformed any single solution.
- Multi-Tiered Approaches: Employing multiple OCR tools created resilience against edge cases.
- Diverse Fraud Detection Methods: Both visual pattern analysis and background similarity checks are vital for comprehensive fraud detection.
- Incremental Complexity: Starting simple and progressively adding complexity based on analytics led to significant cost savings.
- Rapid Iteration with Serverless Architectures: Leveraging AWS’s serverless capabilities allowed for quick modifications and deployments without downtime.
Road Ahead: Expansion and New Opportunities
Looking to the future, Sun Finance plans to expand its operations further, refining its fraud detection capabilities and integrating additional signals. By harnessing the insights gained from this project, they aim for continued scalability, flexibility, and adoption across diverse markets.
In conclusion, the collaboration between Sun Finance and AWS has paved the way for a next-generation identity verification process, showcasing how innovative technologies can drastically improve speed, accuracy, and cost efficiency in fintech operations. This endeavor exemplifies how generative AI can transform foundational workflows, ensuring that applicants receive timely evaluations while simultaneously fortifying anti-fraud mechanisms.
Join the Conversation!
We encourage you to share your thoughts and experiences with document processing and fraud detection in the comments below. How could these technologies impact your organization?
For more information on generative AI in document processing and the solutions offered by AWS, visit the Amazon Bedrock product page or connect with the AWS Generative AI Innovation Center. Let’s reshape the future of fintech, one application at a time!