Using machine learning to support operational decision-making
Business services / software & IT
Operational AI
A North American business services and software provider wanted to use machine learning to support high-volume operational decision-making while retaining human oversight.
Our client operates wearable monitoring devices used in offender monitoring programmes, generating large volumes of operational alerts that must be reviewed by trained operators.
Given the importance of human oversight in these programmes, alerts are assessed by experienced operators who determine whether a situation requires further investigation or escalation.
DataDiligence partnered with the company to develop a machine learning model designed to support operators in assessing incoming alerts. Rather than replacing human judgement, the model analyses patterns within the monitoring data and provides additional insight to help operators review alerts more efficiently and consistently.
The solution provides analytical context and prioritisation signals that assist operators when reviewing alerts, helping teams focus attention on the cases most likely to require investigation.
This approach enhances the existing review process while maintaining full human oversight of operational decisions.
WHAT THIS MEANS FOR THE BUSINESS
Improved decision support for operators reviewing monitoring alerts.
Greater consistency in how alerts are assessed and prioritised.
Better use of operational resources by helping teams focus on the most relevant cases.
FUTURE READINESS
A scalable analytical capability that can support additional operational processes while maintaining human oversight.