# TD Bank Cuts Mortgage Approvals to Three Minutes > Source: > Published: 2026-05-28 19:39:07.289189+00:00 # TD Bank Cuts Mortgage Approvals to Three Minutes PYMNTS reports that TD Bank said agentic AI reduced mortgage pre-adjudication cycle times from roughly **15 hours** to **3 minutes** during its May 28 earnings call. PYMNTS also reports the bank said more than **40,000** employees use internal Copilot tools and more than **7,000** engineers use AI in software development workflows. The same coverage notes U.S. proprietary credit card balances rose **18%** year over year and new bank card account acquisition increased **32%**, per TD executives cited by PYMNTS. Editorial analysis: Industry observers following bank deployments will watch how institutions balance throughput gains with governance, auditability and credit-risk controls. ### What happened PYMNTS reports that TD Bank said agentic AI reduced mortgage pre-adjudication cycle times from roughly **15 hours** to **3 minutes** on its May 28 earnings call. PYMNTS also reports TD said more than **40,000** employees are using internal Copilot tools and more than **7,000** engineers are using AI in software development workflows. PYMNTS further reports U.S. proprietary credit card balances rose **18%** year over year and that TD's executives said new bank card account acquisition increased **32%** following the Nordstrom conversion. ### Technical details Editorial analysis - technical context: Public coverage frames the reduction in pre-adjudication time as an application of agentic AI to orchestrate data retrieval, rule evaluation and automated decision steps that previously required manual handoffs. Industry-pattern observations note that similar pipelines typically combine retrieval from core systems, structured rule engines, and LLM-driven document parsing or decision logic, which raises practical needs for latency tuning, data lineage, and deterministic checks at decision gates. ### Context and significance Industry context: Large retail banks reporting broad internal Copilot adoption and large speedups in underwriting workflows indicate a shift from experimental pilots to production-scale automation. For practitioners, this increases emphasis on operational concerns that follow deployments at scale: throughput monitoring, explainability for credit decisions, integration with fraud engines, and change-management for regulated reporting. ### What to watch Editorial analysis: Observers should track measurable outcomes TD and peers disclose over time-error rates, appeals or reversals, regulatory inquiries, and model governance artifacts such as versioning and audit logs. Also watch whether vendors or in-house platforms publish post-deployment benchmarks that separate latency gains from end-to-end accuracy and compliance tradeoffs. ## Scoring Rationale This is a notable production deployment by a major bank showing dramatic latency improvements, relevant to practitioners deploying AI in regulated financial workflows. It is not a frontier-model release, but it signals meaningful operational impact and governance tradeoffs. Practice with real Banking data 90 SQL & Python problems · 15 industry datasets [Suspicious Online TransactionsEasy](/problems/sql/suspicious-online-transactions) [Delinquent Loans Over 30 DaysMedium](/problems/sql/delinquent-loans-over-30-days) [Credit Card Utilization Risk ReportHard](/problems/sql/credit-card-utilization-risk-report) 250 free problems · No credit card [See all Banking problems](/problems/datasets/banking)