AI-Searched Public Records Alone Insufficient for Title Decision-Making: Study – Commercial Observer
Artificial intelligence-only searches of public records aren’t enough to provide reliable title decision-making at scale, according to a new study by DataTrace, a national provider of property and ownership data and title automation solutions.
The company’s analysis, “AI Title Search Tested in the Real World: What Accuracy, Risk and Readiness Really Look Like,” compares AI-only with title plant-supported searches to evaluate completeness, accuracy and insurability. (A title plant is an index of all of the recorded events that affect a property, unique in that it’s posted, or indexed, as the official legal description of the property.)
“The study is a projection of potential risk, and the opportunity to look at specific — what we would call, gaps — where the AI solution alone was used,” said Annette Cotton, senior vice president and chief data officer at DataTrace. “It predicts if that property had been insured upon, for instance, what could have been an outcome in terms of not being able to report on, address, or remediate that gap.
“Our analysis demonstrates that AI performs best when it operates on a foundation of structured, validated title data, rather than fragmented county-level public records. The speed of AI creates value, but only when paired with the confidence, completeness and accuracy needed for insurable title decisioning. The future isn’t AI versus title professionals. It’s AI powered by trusted title data and guided by experienced title experts. That’s how the industry scales automation without sacrificing confidence or insurability.”
DataTrace manages more than 2,000 title plants nationally.
The new study is a follow-up to DataTrace’s first white paper on its position concerning title plant value, said Cotton. “This was intended to try to answer more details about that and what the impact would have been based on those early findings when we were doing the AI testing against traditional examination.”
Among the current study’s findings, in a review of 200 randomly selected residential title files, AI-only title searches missed at least one meaningful matter in 40.8 percent of searchable files. When compared with title search supported by DataTrace title plant data, these misses highlighted significant gaps in accuracy. Of the 200 files evaluated, AI was unable to search 16 files (8 percent), because it lacked the title plant data or comparable normalized datasets needed to complete the search.
The most significant gaps occurred in high-risk categories, such as involuntary liens, which had an issue fail rate of more than 36 percent.
Philip Russo can be reached at prusso@commercialobserver.com.
