![[D] Built a US Mortgage Underwriting OCR System With 96% Real-World Accuracy → Saved ~$2M Per Year](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8295-300x171.png)
A custom-built document processing system for a US mortgage underwriting firm has achieved around 96% field-level accuracy in real-world applications, significantly surpassing the typical 70-72% accuracy of standard OCR services. This system was specifically designed to handle US mortgage underwriting documents such as Form 1003, W-2s, and tax returns, using layout-aware extraction and document-specific validation. The improvements have led to a 65-75% reduction in manual review efforts, decreased turnaround times from 24-48 hours to 10-30 minutes per file, and saved approximately $2 million annually in operational costs. The success underscores that many AI accuracy issues in mortgage underwriting are rooted in data extraction challenges, and addressing these can lead to substantial efficiency gains and cost savings. Why this matters: Improving data extraction accuracy in mortgage underwriting can drastically reduce costs and processing times, enhancing efficiency and competitiveness in the lending industry.
