One of the most significant potential benefits of AI lies in its ability to transform due diligence in the secondary market. Historically, the process of securitization has relied on loan sampling. Because it was too expensive and time-consuming to manually review every single loan in a massive pool, firms would review a representative sample—often around 30% to 40%—to estimate the risk of the entire pool.
Curran points out that this traditional sampling method contains inherent blind spots that can lead to an understatement of risk. In a typical securitization, loans are graded from A to D. Often, only the highest-rated loans (A and B) are selected for the final pool, while C and D grades are excluded. However, if the initial sample showed a high rate of C and D grades, that risk profile should theoretically apply to the unreviewed portion of the pool as well.
For example, in a pool of 1,200 loans, if a 600-loan sample reveals a 33% rate of C or D grades, the remaining 600 unreviewed loans likely share that same risk profile. If an issuer then creates a deal using only the "clean" loans from the sample plus the unreviewed loans, the rating agency might only see the high grades of the reviewed portion, potentially missing the systemic risk present in the unreviewed half.
AI offers a solution to this transparency gap. By combining traditional third-party reviewer (TPR) audits with AI-driven reviews, firms can achieve 100% coverage. AI can extract data and run it through compliance and credit calculation engines for every single loan in a pool. If the AI flags an issue in a loan that wasn’t part of the manual sample, a human expert can then step in to perform a targeted review. This hybrid approach ensures that no "D-grade" loans slip through the cracks, providing a more accurate risk assessment for rating agencies and investors alike.
Why quality matters more than speed or price
As the industry moves toward broader AI adoption, a divide is forming between firms that prioritize long-term value and those that prioritize speed to market. Curran expresses concern over the lack of industry-wide standards for evaluating AI models. In the current "gold rush" for automation, vendors can release models quickly, claiming high accuracy without having undergone the rigorous testing that firms like SitusAMC require.
"What scares me most is that there’s no industry-wide way to determine whose AI models or data sets are actually better," Curran says. Without standardized benchmarks, it is difficult for lenders to know if the answers they are getting are truly accurate.
The stakes of this uncertainty are high. If an inaccurate AI model is used in the secondary market to evaluate loan quality, it could lead to loans being rated more highly than they deserve. These overvalued assets are then sold to investors, creating a ripple effect of risk that can compromise the stability of the market.
For Curran, the path forward is defined by a commitment to quality over convenience. At SitusAMC, the threshold for releasing an AI capability into a live workflow is a minimum of 95% accuracy—a benchmark that can take months of parallel testing and "soft launches" to achieve. Even after a tool goes live, the process of manual spot-checks and regression testing must continue indefinitely.
The integration of artificial intelligence into the mortgage industry is not a race to be won by the fastest or the cheapest. It is a meticulous process of blending cutting-edge technology with decades of human experience. For the lenders and investors who get it right, AI promises a future of unprecedented clarity and efficiency. For those who cut corners, the risks to the borrower, the lender, and the broader financial ecosystem are too great to ignore. Accuracy, supported by human judgment, remains the only viable currency in the mortgage lifecycle.
