AI USE CASE
Employee Theft Pattern Detection via POS
Detect internal theft early by analysing POS anomalies and access logs with ML.
What it is
Machine learning models analyse point-of-sale transaction patterns, void and refund ratios, and access logs to surface suspicious employee behaviour automatically. The system flags high-risk cases for LP investigators rather than replacing human judgement.
Data you need
At least 12 months of POS transaction logs including void, refund, and discount events, combined with employee shift schedules and store access logs.
Required systems
- erp
- ecommerce platform
Why it works
- Involve loss prevention, HR, and legal stakeholders from the start to define acceptable use policies.
- Use a supervised model seeded with confirmed past theft cases to improve precision.
- Set up a clear escalation workflow so flagged cases reach investigators within 24 hours.
- Review and retrain the model quarterly as transaction patterns evolve seasonally.
How this goes wrong
- Model generates too many false positives, eroding investigator trust and causing alert fatigue.
- Insufficient historical POS data or inconsistent data quality leads to poor model accuracy.
- Lack of integration between POS, HR scheduling, and access control systems creates data silos.
- Legal and HR teams are not involved early, resulting in unusable evidence or compliance issues.
When NOT to do this
Do not deploy this system if your POS data is spread across incompatible legacy systems with no unified transaction ID, as data reconciliation alone will consume the entire budget without producing a working model.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.