AI can help mortgage lenders process documents, verify borrower information, assess credit and eligibility, and estimate a property’s value—but those are distinct tasks, governed by different rules. In title insurance, the documented underwriting process centers on examining title evidence and resolving issues; the available sources describe possible opportunities for automation, not broad or verified AI deployment by title insurers.
What the evidence says about mortgage lenders’ AI adoption
Fannie Mae’s Q3 2023 Mortgage Lender Sentiment Survey found that some surveyed lenders had adopted or were trying AI and machine learning (ML), while others expected to expand their use. These are survey results from 2023, not a measure of the share of U.S. lenders using AI in 2026.
| Finding | Result and context |
|---|---|
| Familiar with AI/ML | 65% of lenders surveyed by Fannie Mae in Q3 2023 said they were familiar with AI/ML. |
| Had deployed AI/ML or were trial users | 30% of lenders surveyed by Fannie Mae in Q3 2023 reported deployment or trial use. |
| Expected broader rollout or to begin trials | 55% of lenders surveyed by Fannie Mae in Q3 2023 anticipated broader rollout or beginning trials within two years of the survey. |
Fannie Mae identified operational efficiency as a leading adoption objective. The survey also described areas lenders recommended for development; those recommendations should not be read as proof that the tools are in widespread use.
Where AI could fit in a mortgage loan file
Application documents and borrower information
Mortgage applications involve documents and data from borrowers and third parties. Software using AI or ML could extract and classify information, compare records, and flag inconsistencies for review. In its 2023 survey, Fannie Mae identified borrower income and employment verification and documentation reconciliation and standardization as potential development areas. A flagged mismatch can prompt investigation; it does not establish by itself that an applicant supplied false information or is ineligible.
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Credit risk and eligibility: automated underwriting systems
An automated underwriting system (AUS) evaluates an applicant’s credit risk and whether a loan meets eligibility requirements for the relevant securitizer, insurer, or guarantor, as described in the Consumer Financial Protection Bureau’s Regulation C interpretation. In applicable Home Mortgage Disclosure Act (HMDA) reporting, the lender may have to report the AUS name and the result it generated. This reporting requirement does not require lenders to use an AUS: the CFPB’s interpretation says an application underwritten manually, with no AUS used, is reported as not applicable for that field.
An AUS result is not the same as an AI model’s estimate of what a home is worth. The system roles and the questions they address differ.
Collateral value: automated valuation models
An automated valuation model (AVM) estimates a property’s value from data and a model. That estimate concerns collateral, not the applicant’s credit risk or loan eligibility. Fannie Mae’s survey discussed property valuation and appraisal automation as areas for development, but the survey’s recommendations do not establish how widely particular valuation tools have been deployed.
Compliance support and anomaly detection
Fannie Mae’s survey also identified compliance management and anomaly detection among possible or recommended mortgage AI applications. These tools may help staff identify records or patterns for examination. They should not be treated as autonomous legal determinations or as a substitute for the lender’s responsibility to apply relevant requirements.
What federal controls apply to covered AVM uses
A federal interagency rule establishes quality-control standards for AVMs used in certain transactions involving the collateral value of a consumer’s principal dwelling. The adopting agencies are the Office of the Comptroller of the Currency, Federal Reserve Board, Federal Deposit Insurance Corporation, National Credit Union Administration, Consumer Financial Protection Bureau, and Federal Housing Finance Agency. FHFA lists October 1, 2025, as the rule’s effective date.
For covered uses, institutions must adopt policies, procedures, practices, and control systems designed to:
- Ensure a high level of confidence in estimates produced by AVMs.
- Protect against the manipulation of data.
- Seek to avoid conflicts of interest.
- Require random sample testing and reviews.
- Comply with applicable nondiscrimination laws.
This rule is scoped to specified AVM uses. It is not a blanket certification standard for every AI tool used in mortgage lending, and an AVM control does not turn a property estimate into an underwriting decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What title-insurance underwriting involves
Title-insurance underwriting concerns the title evidence and the risks associated with insuring a property’s title. The CFPB’s Regulation Z interpretation describes title-insurance services as examining and evaluating title evidence under applicable law and underwriting principles, preparing a commitment that states proposed insured status and conditions, resolving underwriting issues, and preparing and issuing policies. Fannie Mae’s Selling Guide has a dedicated title-insurance chapter setting out lender requirements and coverage topics.
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Because the process involves records and document review, extraction of information, record matching, exception identification, or routing a file to a reviewer are conceivable automation points. These are potential applications, not evidence that title insurers broadly use AI for title searches, chain-of-title review, defect detection, or issuing commitments. The cited workflow and lender guidance establish what the work involves, but do not provide title-specific deployment rates or identify live AI systems at title companies.
Who remains accountable when an insurer uses AI
The National Association of Insurance Commissioners (NAIC) describes AI use across insurance functions that include underwriting, pricing, customer service, claims, marketing, and fraud detection. Its general insurance overview emphasizes that insurers remain responsible for compliance with applicable insurance laws, regulations, and consumer-protection requirements when decisions are supported by AI. It also describes state regulators’ interest in how systems are used and governed, how risks are mitigated, and what models and data inputs they rely on. This is general insurer oversight, not a title-insurance-specific AI rule.
The NAIC page updated April 3, 2026, reported that its AI Systems Evaluation Tool was being piloted by 12 states as of March 2026 and was anticipated for consideration at the NAIC 2026 Fall National Meeting. That is a dated status report, not confirmation of the tool’s status after the meeting.
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