HarrisLogic Uses SAP Predictive Analytics for Recidivism Prediction

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HarrisLogic Uses SAP Predictive Analytics for Recidivism Prediction

Case study showing how HarrisLogic integrated SAP Predictive Analytics and HANA to predict repeat jail admissions for individuals with mental health conditions using data from seven sources.

Published

June 4, 2026

Read time

3 min read

Source

SAP

HarrisLogic built a predictive model on SAP HANA and SAP Predictive Analytics to forecast whether previously incarcerated individuals with mental health conditions would return to jail within six months. The system ingests mental health and criminal justice records, flags high-risk individuals in real time, and supports resource allocation for clinicians and law enforcement. Results include 99% prediction accuracy, 25% reduction in behavioral health crisis spend, and 89% average crisis diversion rate.

Key takeaways

SAP Predictive Analytics on HANA delivered 99% accuracy in predicting six-month recidivism for mental health populations.

Integration of seven disparate data sources enabled real-time risk scoring on incoming jail records.

Native logistic regression models and drill-down analytics support ad hoc analysis and clinician decision support.

Deployment achieved 25% reduction in behavioral health crisis spending and 89% crisis diversion rate.

Future expansion targets higher education and additional crisis service markets.

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