Transforming Mortgage Underwriting with AI-Driven Bank Statement Analysis

Challenge

A non-QM lender analyzing bank statements to estimate self- employed borrowers’ income sought a strategic advantage in speeding up the process and providing quicker conditional approvals. The existing manual analysis was time-consuming, required extensive staff training, and struggled to scale during peak application periods. While an AI-driven platform could automate the process, it needed near-perfect data capture and fraud detection to be effective.

DDD’s Solution

We implemented a human-augmented document scanning process that achieved over 99.9% accuracy by comparing captured data against original bank statements and correcting any discrepancies through a machine-learning loop. This iterative process improved data accuracy with each cycle. For three months, manual statement analysis continued alongside the automated system, allowing underwriters to provide feedback to refine the fraud detection model.

Harnessing AI to Accelerate Mortgage Applications, Improve Accuracy, and Scale Operations 

Impact

The manual process, which previously added three business days to mortgage underwriting, was replaced with a fully automated system that reduced processing time to under three hours per application. This shift enabled underwriters to focus on full-file underwriting, increasing loan production capacity without additional headcount.