Schools across the United States suffer from low on-time graduation rates. Targeted interventions help at-risk students meet graduation requirements in a timely manner, but identifying these students takes time and practice, as warning signs are often context-specific and reflected in a combination of attendance, social, and academic signals scattered across data sources. Extremely high caseloads for counselors compound the problem. At Infinite Campus, a large student information system provider, we modeled statistical relationships between student educational records and enrollment outcomes, using de-identified records and in-system analysis to guarantee student data privacy. The resulting risk scores are highly predictive, context-sensitive, nationally available, integrated into the existing student information system, and updated daily. [For the full proceedings, see ED599096.]