AI governance, audit trails, deal-tech, the limits of generic AI and the value of deal-trained tools. The firm’s AI-skeptic editorial spine; DealGPS is the natural counter-point.
No, and the reason is narrower and more checkable than the usual objection to AI in legal work. A general-purpose model is trained on public text. The acknowledgment agreement is a form you request from Fannie Mae’s Mortgage Servicing Rights Pledges division rather than a document that circulates, the provisions that decide who gets paid…
Can a general-purpose AI model review a mortgage loan purchase agreement? No — not competently, and not because the model reasons badly. The obstacle is evidentiary. A general model learns from public text, and the provisions that decide a secondary-market trade’s legal economics are almost never written about in public. The model has not read…
What are the financial and legal risks of using AI to draft MLPA compliance representations? Relying on artificial intelligence to draft or approve compliance-with-law representations in a Mortgage Loan Purchase Agreement (MLPA) exposes firms to massive multi-year repurchase tails and unhedged balance-sheet liabilities. Because AI tools lack the capability to ground language in active appellate…
How do un-auditable AI underwriting decisions lead to loan indemnification demands, and how do Fannie Mae and Freddie Mac regulations impact this? Under internal agency frameworks like Fannie Mae’s Lender Letter LL-2026-04, mortgage lenders are strictly required to operate auditable AI governance programs that track vendor usage, annual compliance audits, and individual loan-level data tracking….
