AI can help mortgage lenders process documents, verify borrower information, support risk analysis, and estimate property values—but an automated underwriting system (AUS) and an automated valuation model (AVM) perform different jobs. Title insurers also have document-intensive underwriting workflows, but available evidence does not establish how widely they use AI or identify title-specific systems in operation.
What lender adoption figures actually show
Fannie Mae’s Q3 2023 Mortgage Lender Sentiment Survey measured respondents’ familiarity with AI and machine learning (ML), reported use, and plans at that time. It is a dated survey, not a measure of the market in 2026.
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| Survey finding | What it means |
|---|---|
| 65% were familiar with AI/ML | Fannie Mae survey respondents in 2023; familiarity does not mean a lender had deployed a system. |
| 30% had deployed AI/ML or were trial users | Fannie Mae survey respondents in 2023; the figure combines deployed tools with trials. |
| 55% anticipated broader rollout or beginning trials within two years | Fannie Mae survey respondents in 2023 describing plans, not proof that those plans were later completed. |
Operational efficiency was a leading adoption objective in the survey. These figures describe surveyed lenders, not every lender, and should not be read as current adoption rates.
Where AI may fit in a mortgage loan workflow
Application documents and borrower information
Mortgage files contain borrower-provided records and information from third parties. Software using AI or ML could extract and classify information, compare documents with one another, or flag inconsistencies for review. Fannie Mae’s survey identified borrower income and employment verification, and documentation reconciliation and standardization, as recommended areas for mortgage AI development. Those recommendations do not establish that every lender uses such tools.
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Credit risk and loan eligibility
An AUS evaluates an applicant’s credit risk and whether a loan meets eligibility requirements for the relevant securitizer, insurer, or guarantor. The term describes a system role; it does not, by itself, show that the system uses AI or ML. Under the Consumer Financial Protection Bureau’s Regulation C reporting provisions, applicable mortgage application records may need to identify the AUS and report its result. That reporting rule does not require lenders to use an AUS: a manually underwritten application with no AUS is reported as not applicable for that field.
Compliance and risk-support tasks
Fannie Mae’s survey discussed compliance management, default-risk and prepayment assessment, and anomaly detection as potential or recommended application areas. Such tools can help surface patterns or items for attention; the survey does not establish that they make autonomous legal determinations or that lenders broadly deploy them.
An AUS is not an AVM
An AUS assesses borrower and loan eligibility; an AVM estimates a property’s value. A property estimate may inform collateral review, but it is not the AUS credit-risk and eligibility result. The distinction matters because the systems answer different questions and are subject to different regulatory provisions.
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|---|---|---|
| AUS | How does the application fare on credit risk and eligibility criteria? | CFPB Regulation C defines the system for applicable reporting. It does not mandate use of an AUS. |
| AVM | What is an estimate of the property’s value? | Federal quality-control standards apply to covered AVM uses involving the collateral value of a consumer’s principal dwelling. |
| Title-insurance underwriting | What does the title evidence show, what conditions or issues must be addressed, and what coverage can be offered? | CFPB’s Regulation Z interpretation describes the services; the cited sources do not establish title-insurer AI adoption levels. |
Controls for covered automated property valuations
Six agencies—OCC, Federal Reserve Board, FDIC, NCUA, CFPB, and FHFA—adopted quality-control standards for AVMs used in certain transactions involving the collateral value of a consumer’s principal dwelling. FHFA lists October 1, 2025, as the rule’s effective date. Covered institutions must adopt policies, practices, procedures, and control systems designed to:
- Ensure a high level of confidence in estimates.
- Protect against manipulation of data.
- Seek to avoid conflicts of interest.
- Require random sample testing and reviews.
- Comply with applicable nondiscrimination laws.
This rule concerns covered AVM uses. It is not a blanket certification standard for every AI tool used in mortgage lending.
What title-insurance underwriting involves—and what is known about AI
Title-insurance underwriting examines evidence about a property’s title and determines the proposed insured status, conditions, and coverage. The CFPB’s Regulation Z interpretation describes services that include examining and evaluating title evidence under applicable law and underwriting principles, preparing a commitment that states proposed coverage and conditions, resolving underwriting issues, and preparing and issuing policies. Fannie Mae’s Selling Guide also has a dedicated title-insurance chapter setting out lender requirements and coverage topics.
Because this work involves records and document review, extraction, record matching, exception identification, or workflow routing are plausible points where automation could assist. That is a description of possible applications, not evidence that title insurers broadly use AI for title searches, chain-of-title review, defect detection, or issuing commitments. The cited sources do not establish title-specific deployment rates or identify named live AI systems at title companies.
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Accountability does not disappear when AI assists
The National Association of Insurance Commissioners (NAIC) describes AI use across insurance functions such as underwriting, pricing, customer service, claims, marketing, and fraud detection. Its overview stresses that insurers remain responsible for compliance with applicable insurance laws, regulations, and consumer-protection requirements when AI supports decisions. State regulators are examining how systems are used and governed, how risks are mitigated, and what models and data inputs they rely on. This is general insurance oversight, not a title-insurance-specific AI rule.
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