The future of mortgage underwriting: How AI prepares you for the housing rebound

The AI-driven future of mortgage underwriting

The mortgage industry is experiencing a shift, with rising origination costs and an impending market rebound creating a need for greater efficiency. AI-driven mortgage underwriting is no longer a future aspiration—it’s a present-day necessity for lenders looking to streamline operations, reduce errors and scale effectively without increasing staffing costs.

In this session, Ocrolus mortgage experts Rebecca Seward and David Gibson discuss how AI is reshaping underwriting, eliminating inefficiencies and positioning lenders for long-term success.

Key takeaways

• Market volatility is driving lenders to seek efficiency. Loan origination costs have increased 35% since 2020, making AI-driven automation essential to scaling without adding staff.
• AI is shifting from borrower experience enhancements to operational efficiency. The industry focus is now on automating underwriting, document handling and income calculations to improve speed and accuracy.
• Lenders who fail to prepare for the next market rebound risk falling behind. AI enables proactive scalability, reducing reliance on hiring and firing cycles tied to market fluctuations.
• AI doesn’t replace underwriters—it enhances their efficiency. By automating repetitive tasks like document classification and data transcription, AI allows underwriters to focus on risk assessment and decision-making.
• Successful AI adoption requires company-wide buy-in. Change management best practices include setting clear KPIs, establishing a performance baseline, piloting adoption with early adopters and providing ongoing training.

How AI is transforming mortgage underwriting

1. Reducing costs and increasing scalability

Lenders historically relied on hiring additional staff during market upswings and downsizing during downturns. AI-driven automation provides a sustainable, scalable alternative by handling high-volume tasks without increasing overhead.

Example: HomeTrust Bank implemented AI-powered document automation, saving 8,500 hours across loan processing teams and reducing operational costs by $90,000 annually.

2. Eliminating manual, time-consuming tasks

AI enhances underwriting efficiency by automating:

• Document sorting and classification – Automatically identifying and organizing mortgage files.
• Income verification and calculations – Extracting borrower financials without manual input.
• Fraud and discrepancy detection – Identifying mismatched borrower data across documents.

3. Enhancing loan quality and decision accuracy

AI doesn’t replace human judgment—it augments it. Underwriters trust but verify AI-generated insights, leading to more accurate, compliant and faster decision-making.

Best practices for AI adoption in mortgage lending

• Set clear business goals and KPIs – Define success metrics before implementation.
• Establish a performance baseline – Measure pre-AI efficiency levels to track improvements.
• Analyze existing workflows – Identify where AI can eliminate bottlenecks.
• Start with a pilot program – Test new technology with a small group before full-scale deployment.
• Invest in training and ongoing education – Ensure all team members understand the role of AI in underwriting.

The competitive advantage of AI in mortgage lending

The next 12 to 18 months will bring increased market activity. Lenders who prepare now with AI-driven underwriting will outpace competitors, reduce inefficiencies and improve borrower experiences.

Ocrolus empowers lenders with AI-powered underwriting solutions that drive efficiency, accuracy and scalability. If you're attending the MBA Annual Conference, visit our team for an exclusive demo of our newest AI mortgage technology.

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