Turning hidden retail losses into measurable and actionable intelligence
A data and analytics solution that brought transaction, inventory and operational signals together to identify unusual activity, prioritize high risk cases and help loss prevention teams investigate issues faster.
Retail losses were visible in the numbers, but the underlying patterns were difficult to identify at scale.
Shrinkage can result from theft, fraud, transaction exceptions, inventory discrepancies and process leakage. Reviewing each transaction or store manually does not scale across a large retail network.
What we found
- Loss prevention teams had to work across multiple systems to investigate unusual activity.
- POS transactions, inventory movements, refunds and operational information were not always available in one analytical view.
- Manual reviews focused on individual transactions instead of patterns across stores, products and time periods.
- High volume alerts made it difficult to distinguish routine exceptions from cases that needed immediate investigation.
- Repeated refunds, discounts and price overrides could be difficult to compare across stores and employees.
- Inventory discrepancies were often investigated after the loss had already occurred.
- Investigation outcomes were not consistently fed back into the analytics process to improve future prioritization.
What the business needed
- A unified view of transaction and inventory related signals.
- Automated detection of unusual behavior and operational patterns.
- Risk scores to prioritize cases based on potential business impact.
- Store, product, transaction and time based drill down for investigators.
- Clear explanations for why an activity was flagged.
- Integration of computer vision signals where camera data was available.
- Outcome tracking to measure recovered value and improve detection rules over time.
We created a loss intelligence platform that moved teams from broad manual review to prioritized investigation.
The solution combined structured retail data with anomaly detection and optional computer vision signals to identify patterns that warranted investigation.
Retail loss intelligence engine
The platform established a common analytical layer across transactions, inventory and store operations and used risk scoring to direct attention to the highest priority cases.
- Integrated POS transactions, refunds, discounts, price overrides, inventory movements, product and store information.
- Built behavioral baselines for stores, transaction types, product groups and operational activities.
- Used anomaly detection to identify unusual refund, discount, transaction and inventory patterns.
- Created risk scores based on frequency, deviation from normal behavior, financial exposure and historical patterns.
- Grouped related events into investigation cases rather than generating disconnected alerts.
- Added computer vision inputs for shelf gaps, unusual activity or other available visual signals where the operating environment supported it.
- Connected cases to investigation workflows and tracked outcomes, actions and recovered value.
A connected loss prevention data layer across transactions, inventory and store operations
The architecture allows multiple sources to contribute evidence to the same investigation rather than treating each operational signal separately.
A six stage approach to build a measurable loss prevention capability
The implementation started with data visibility and high value analytical patterns before expanding into advanced detection and operational workflows.
Assess
Map shrinkage drivers, investigation processes, systems, loss categories and existing controls.
Connect
Integrate POS, refunds, inventory, product, store and operational data.
Baseline
Establish expected transaction and inventory behavior by store, product and process.
Detect
Apply anomaly detection, business rules and risk scoring to identify unusual activity.
Investigate
Prioritize cases and provide supporting evidence for loss prevention teams.
Improve
Track outcomes, recovered value and false positives and refine detection continuously.
Loss prevention teams could focus on the exceptions most likely to require action.
Reduction in identified shrinkage
Improved detection and prioritization helped teams identify and address loss patterns earlier.
Reduction in investigation effort
Risk based prioritization reduced the volume of low value manual reviews.
Faster identification of high risk activity
Automated monitoring surfaced unusual patterns without waiting for periodic manual analysis.
Reduction in false positive reviews
Risk scoring and contextual signals helped investigators focus on more meaningful cases.
Faster case triage
Investigators received prioritized cases with transaction and store context in one view.
Improvement in exception visibility
Combining transaction and inventory signals exposed patterns that were difficult to see in individual systems.
Loss prevention became a proactive analytics capability rather than a retrospective review process.
The solution helped retailers identify where losses were occurring, understand the signals behind them and direct investigation resources toward the highest priority cases.
Protecting Margin
Earlier identification of loss patterns helped reduce leakage that directly affects store and enterprise profitability.
Faster Investigation
Investigators could start with prioritized cases and supporting evidence instead of searching across multiple systems.
Better Operational Control
Repeated refunds, discounts, overrides and inventory mismatches could be monitored consistently across the retail network.
Scalable Monitoring
Automated detection allowed teams to monitor large transaction volumes and focus human effort on cases requiring judgement.
Turn retail data into action against operational leakage.
From transaction and inventory intelligence to anomaly detection, computer vision and investigation workflows, a connected loss prevention platform can help retailers protect revenue and improve margins.
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