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TD’s agentic AI is helping prepare mortgage applications for human underwriters; it does not make final mortgage approval decisions, according to the bank. Launched on May 21, 2026, the system handles pre-adjudication work for mortgage and Home Equity Line of Credit (HELOC) applications. TD says early results cut the average time for that process from 15 hours to less than three minutes, though it has not published the sample size or measurement method.
What TD’s mortgage AI does
TD Bank Group introduced the system in Real Estate Secured Lending (RESL), its mortgage and home-equity lending business. The agentic AI model processes application documents and prepares a concise summary memo for a human underwriter. This is pre-adjudication: it supports the underwriting workflow rather than replacing the underwriter or issuing a final approval.
TD says its Layer 6 AI research and development centre developed the model with the bank’s technology, data, RESL, and risk-management teams. The system is designed to:
- Classify documents submitted by clients and extract key information.
- Calculate income and validate figures against selected policy requirements.
- Perform consent checks and search for discrepancies.
- Compile relevant findings into a summary for the underwriter.
TD’s May 21 announcement describes this as the first step toward applying agentic AI across the RESL journey, from document submission through funding. It does not establish that every stage—or every mortgage and HELOC application—has been automated. TD Bank Group’s launch announcement
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How much faster is the pre-adjudication process?
TD reported an early-results change in average pre-adjudication time from 15 hours to less than three minutes. That figure applies to the preparation stage described by the bank, not the total time a customer waits for a mortgage decision or funding. TD’s announcement does not give a sample size, detailed measurement design, or independent validation, so the result should be treated as a company-reported early measure rather than a guaranteed turnaround time.
In a July 31, 2026, feature, TD said 100% of new personal mortgages were then going through the process. That is a dated claim with a specific scope: it does not establish that all mortgage types or HELOC applications used the system, or that the same rollout percentage remains current. TD Stories’ July 2026 account
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- DEDICATED BUYER QUALIFYING KEYS: Enter client's income, debt and expenses to pre-qualify them to only show properties they can afford. Include tax, insurance and mortgage insurance then compare loan options and payment solutions to give your client choices before they make an offer to buy
- FIGURE OUT THE RIGHT LOAN: At the press of a button for jumbo, conventional, FHA/VA, or even 80:10:10 or 80:15:5 combo loans; check to see if ARMs or bi-weekly loans, quarterly payments or if interest-only payments are the answer; giving your client more choices; easily perform what if loan or tvm calculations Find loan amount, term, interest or PITI or PI payments
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Does the AI approve mortgages?
The material TD has published describes the AI as preparing information for a human underwriter. It does not say the model itself approves or declines mortgage applications. The memo is an input to a human review, and TD’s July account characterizes the solution as human-in-the-loop.
TD’s executives frame the approach as a collaboration between people and AI. Mohit Veoli, Senior Vice President, Real Estate Secured Lending at TD, said: “Agentic AI is enabling us to deliver what clients tell us matters most – speed and simplicity,” He also described the launch as an early stage: “This is just the beginning of how we’re using AI to evolve the mortgage experience and bring our promise of being more human to life.”
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Luke Gee, Chief Analytics and AI Officer at TD, said: “We’re building a hybrid future where our colleagues and AI work together to help our clients get to a ‘yes’ faster on some of their most important decisions.” The remarks describe TD’s intended direction; they are not evidence that the system independently makes lending decisions or that every applicant will receive a faster approval.
What safeguards and oversight does TD describe?
TD says its Trustworthy AI team oversees integration and evaluates models for privacy, security, fairness, accountability, and explainability before they come into contact with humans. The bank also says deployed models are monitored. Its July feature specifically describes the mortgage solution as built under this approach with a human in the loop.
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- SPEAKS YOUR LANGUAGE: Keys clearly labeled in residential mortgage finance terms like Loan Amt, Int, Term, Pmt; this industry-standard calculator is super easy to use on all realty financing matters from finding a loan that works for your client to considering trust deeds investments, or finding remaining balances or balloon payments and more
- CONFIDENTLY AND EASILY SOLVE: Clients' financial questions whether they're buyers, sellers, investors or renters. Increase your perceived professionalism as a new agent, experienced broker or seasoned loan officer. Close more home sales and impress your clients with fast, accurate answers to all their real estate finance questions from PITI Payments to IRR, NPV and Cashflows
- DEDICATED BUYER QUALIFYING KEYS: Enter client's income, debt and expenses to pre-qualify them to only show properties they can afford. Include tax, insurance and mortgage insurance then compare loan options and payment solutions to give your client choices before they make an offer to buy
- FIGURE OUT THE RIGHT LOAN: For your client at the press of a button for jumbo, conventional, FHA/VA, or even 80:10:10 or 80:15:5 combo loans; check to see if ARMs or bi-weekly loans, quarterly payments or if interest-only payments are the answer; giving your client more choices; easily perform what if loan or TVM calculations find loan amount, term, interest or PITI or PI payments
- BECOME AN INVALUABLE RESOURCE: To your clients by reducing their confusion and uncertainty; ensuring they are able to make a purchase offer; knowing they can afford the down payment; and determining which is the right loan for them. Date-math for listings and contracts too. Comes with a protective slide cover, quick reference guide, pocket user's guide, and long-life battery
These are TD’s descriptions of its governance practices. The published accounts do not provide a quantified accuracy improvement, an evaluation method for the claim that results are more accurate, or comparative customer-outcome data. Sandra Aziz, Senior Machine Learning Engineer at Layer 6, described the human-facing purpose of the output: “The agentic AI agent will summarize different information from different data points and provide the right information to a human.”
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TD presents the RESL launch as part of a broader enterprise AI strategy that combines machine intelligence with human expertise, with goals including speed, simplicity, and personalization. The bank’s 2025 annual-report strategy page separately discusses faster mortgage pre-approval, pricing, and application processes in Canadian Personal Banking. That broader strategy statement is distinct from a measured outcome of this specific agentic AI model. TD’s 2025 annual-report strategy page
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TD has also set a corporate goal of generating $1 billion in annual value from AI in the coming years. This is a forward-looking enterprise target, not value already realized and not a mortgage-specific result. In discussing the work, Aziz said: “We have been able to make things move faster. But we also want to be able to make sure that whatever we’re doing makes sense from a client point of view,”
What the published results do—and do not—show
The available evidence comes from TD’s own announcements and feature coverage. It supports the conclusion that the bank has deployed an AI-assisted pre-adjudication workflow and that TD reports a substantial early reduction in average processing time for that stage. It does not establish an independently measured effect on total application time, approval rates, accuracy, fairness outcomes, or customer experience.
For applicants, the practical distinction is important: faster preparation of an underwriter memo may streamline one part of the process, but the sources do not promise a decision in under three minutes. Human underwriting remains part of the workflow TD describes.
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