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Study finds AI-only public records searches miss title matters often
In a test of 200 residential title files, AI-only searches missed at least one meaningful matter in 40.8 percent of cases and could not complete 8 percent due to missing title plant or normalized data.
Artificial intelligence-only searches of public records may not be reliable enough for title decision-making at scale, according to a study from DataTrace, as summarized by Commercial Observer. The analysis, “AI Title Search Tested in the Real World: What Accuracy, Risk and Readiness Really Look Like,” compares AI-only searches with title plant-supported searches to assess completeness, accuracy, and insurability.
A title plant, which is described as an indexed record of events that affect a property and serves as the official legal description, is presented as a structured foundation for AI. DataTrace said AI can add speed, but only when paired with “confidence, completeness and accuracy” needed for insurable decisions, and that automation should be guided by experienced title experts rather than replacing them.
In the study’s testing of 200 randomly selected residential title files, AI-only searches missed at least one meaningful matter in 40.8 percent of the searchable files. The report also found AI could not complete searches in 16 files, or 8 percent, because it lacked title plant data or comparable normalized datasets, with gaps noted as most significant in high-risk categories.
DataTrace’s Annette Cotton, senior vice president and chief data officer, said the work is intended to project potential risk and identify “gaps” where an AI-only approach was used, including how those gaps could affect outcomes such as what can be addressed through insurance and remediation. The study is described as a follow-up to an earlier DataTrace white paper on title plant value and the impact of AI testing versus traditional examination.