AI in mortgages that actually saves time, not hype.On this episode of Mortgage Tech Talks I sit down with Lucas Scheer, co-founder of Purelend. We dig into what AI can do today for brokers, why trust still wins in real estate and lending, and how document collection, usable income, and down payment verification can be automated without losing the human touch.
What you’ll learn
The real advantage for brokers who use AI vs those stuck in manual review
How Purelend structures down payment checks, stated income analysis, and doc organisation for lenders and brokers
Where full automation fails and why a copilot model makes more sense
Accuracy, hallucinations, and how to combine human review with AI to raise overall quality
Practical privacy and workflow considerations when rolling AI into your shop
Chapters00:00 AI will not replace you. People using AI will00:26 Set up for the episode00:40 Intro and guest welcome01:01 What Purelend does for brokers and lenders02:24 Lucas’ background, NEO Financial, and the path to Purelend03:10 The 60,000 km motorcycle detour and lessons04:10 Building applied AI before ChatGPT05:13 Pre-2020 AI adoption vs the post-ChatGPT shift06:19 Focusing on outcomes over buzzwords07:33 Media hype, fear, and what actually matters to a brokerage08:28 Useful analogies for explaining AI to clients09:03 Common fears brokers raise10:11 Two brokers, two results. Why leverage wins11:26 Excel analogy and why AI is the new table stakes12:07 Privacy, data, and realistic risk12:46 Will AI do prospecting and client calls13:39 Why trust and emotion keep humans in the loop15:14 Where automation helps and where it breaks16:20 Purelend elevator pitch16:46 Duplicate effort across brokers and lenders17:46 Speed, cost basis, and winning deals18:10 Why this only became feasible recently19:18 Feature set: down payment, usable income, organising docs20:02 Stated income launch and common gotchas21:06 Borrower alerts without adding friction21:39 Full auto vs copilot. Drawing the line24:12 Specific knowledge that AI cannot replace25:38 Borrower portal vision and UX tradeoffs30:44 Accuracy, hallucinations, and safeguards32:27 Probabilistic vs deterministic and hitting 92 to 96 percent35:04 Benchmarking against human accuracy38:50 Supervisors, different error patterns, and combined accuracy40:38 Where to learn more40:55 Close
Show more...