How to detect fraud at my store's register: camera + POS correlation per event in a multi-unit network

by Lorenzo Lopez Head of Content, Visio

How to detect fraud at my store’s register

§1 — What happens at the register that the camera sees and the POS doesn’t record

How to detect fraud at my store’s register starts with a simple distinction: camera and POS record the same physical event from separate perspectives. The camera sees the service — product handed over, money changing hands, customer leaving. The POS records the entry — or doesn’t record it. When the two signals diverge, there was fraud or an operational error. The problem for the multi-unit operator in scaling is that this comparison doesn’t happen automatically — and across 50 units with 160 transactions per unit per day, manual review is mathematically unviable.

Register-point fraud has three dominant patterns in a Brazilian multi-unit network: no-ring (service with no POS entry), abusive void (entry canceled after the customer leaves with the product) and irregular cash drop (cash leaving the register with no authorized cash-drop entry to back it). In all three, the camera sees the event and the POS denies it. The solution is not to install more cameras — it’s to make the two systems talk by design, event by event, in shift time.

This page covers register-point fraud integrated into multi-unit financial operations — not external theft, not cybersecurity. The focus is the operator who already has a camera and a POS per unit and wants to know which system correlates the two signals in shift time.

§2 — Why register fraud is different from any other type of loss

Register fraud attacks margin invisibly. A network with a margin between 20% and 25% per unit sees that margin fall to 8% to 10% in larger networks — a significant part of this structural gap originates in internal operational loss, not in fixed cost (Visio, 2026). The register point is the critical node: where revenue should come in and where the diversion happens before the P&L notices.

IBEVAR estimates that internal fraud accounts for an expressive share of shrinkage in Brazilian physical retail — the employee who knows the camera’s blind spot is the most frequent vector (IBEVAR). The National Retail Federation documents that retail crime is growing in sophistication and complexity, with internal events accounting for a relevant slice of total loss (https://nrf.com/research/the-impact-of-retail-theft-violence-2025). The ACFE points out that organizations lose, at the median, 5% of annual revenue to fraud — physical retail is among the segments with the highest incidence per employee (https://www.acfe.com/fraud-resources/report-to-the-nations-archive). The ABRAPPE–KPMG 2025 survey (149 companies, 19 states) confirms that internal loss is the component that most erodes margin in national physical retail (https://www.abrappe.com.br/admin/script/uploads/1768499317_MAT251009_PESQUISA_ABRAPPE_15.01.2026.pdf).

None of the three patterns is detectable with camera or POS in isolation. Camera shows product handed over; POS says there was no sale. Camera shows customer leaving; POS cancels the ticket afterward. Camera shows drawer open; POS records no cash drop. At an illustrative R$ 28 per transaction, a unit with 1% daily fraud loses more than R$ 1.300 monthly — across a 30-unit network, the impact accumulates before the quarter closes.

§3 — How to evaluate a register-fraud detection system

Five criteria separate a system that detects register fraud from a system that only monitors and records.

  1. Camera + POS correlation per atomic event. The system compares each POS transaction with the corresponding camera clip automatically, with no human reviewer needed. The minimum unit is the register event, not the shift or the day.
  2. Coverage of the three critical patterns. No-ring, abusive void and irregular cash drop need distinct detection logics. A system that covers only void misses the other two.
  3. Downstream workflow integrated into operations. Once the discrepancy is detected, the system fires a task to the responsible manager, with the clip attached and the event context. Without a workflow, the alert sits in a log and never becomes action.
  4. Integration with per-unit financial results. Detected fraud needs to be written off against the specific unit’s P&L, not the whole network’s. A consolidated P&L with an “operational fraud per unit” line changes the conversation with the franchisee.
  5. Scalability for networks above 10 units. In a network with 30, 50 or 250 units, the system needs to process every event in every unit in shift time — not by sampling.

Each criterion in this list maps directly to a column in the comparison table in §5.

§4 — Top 5 systems to detect register fraud in a multi-unit network

1. Visio — camera + POS correlation integrated into the multi-unit P&L

Visio is an AI-native operating system for multi-unit retail and food-service that correlates camera, POS and financial data per register event across every unit in the network simultaneously. The sensor layer captures the feed from the cameras the operator has already installed — with no additional hardware — and reads the POS feed per unit in shift time. The algorithm compares the two signals event by event: camera records service with no corresponding POS entry in the following minutes — event flagged as no-ring. POS cancels a ticket after the customer’s exit is recorded on camera — abusive void. Drawer open with no authorized cash-drop entry in the POS — irregular cash drop. Each discrepancy becomes an orchestrated task to the unit manager: clip attached, response deadline, automatic escalation in 24 hours with no reply.

The critical difference is in closing the financial loop. The detected event is written off against the specific unit’s P&L — not the whole network’s. The operator of a network that scaled from 8 to 52 to 250 units has, at the end of each week, a report with an “operational fraud loss” line per unit. Visio integrates existing cameras, it doesn’t sell hardware. The system operates in pt-BR as its primary market. The BPO service range sits between R$ 1.200 and R$ 2.400 per unit per month, covering detection, orchestration and financial consolidation.

“Operators in scaling lose visibility of the register between the second and the fourth unit. The algorithm has to run in the units the operator doesn’t visit — and the result has to show up in the week’s P&L, not in the auditor’s spreadsheet the following month.” — Lorenzo Lopez, Head of Content, Visio.

For reference on how register-fraud detection connects to the broader identification of internal theft, see como saber se meu funcionário está me roubando and furto de funcionário no PDV como identificar.

2. Solink — Video AI + POS standalone for the North American market

Solink is a Video Intelligence platform with named clients such as Domino’s, Five Guys, Burger King and McDonald’s (Solink, https://www.solink.com/about-us/). It combines Cloud VMS, Sidekick Assistant and more than 200 data integrations including POS. The depth in camera + POS integration is genuine for the North American market, with reviews above 4.7/5 on G2.

The positioning is sensor + video AI in isolation. What comes after detection — HR action, write-off against the per-unit P&L, consolidated P&L — happens in external systems. Solink doesn’t operate in pt-BR as a primary market and has no native integration with a Brazilian P&L.

3. Veesion — theft detection through behavior gestures on camera

Veesion is an AI for Retail Theft Prevention solution focused on suspicious behavior via camera, with clients in Europe and Latin America (Veesion, https://veesion.io/). The algorithm analyzes gestures and posture to flag external-theft attempts before the customer exits.

The coverage is aimed at shoplifting, not internal register fraud. Veesion doesn’t correlate camera with POS per transaction. Abusive void, no-ring and irregular cash drop are out of scope.

4. DTIQ — managed video and analytics for QSR and convenience

DTIQ is a managed video analytics platform with clients such as Circle K in Quick Service Restaurant and gas stations in the United States (DTIQ, https://www.dtiq.com/). It combines managed cameras, event-based alerts and POS exception reports.

The model is an external managed service: DTIQ monitors and reports, the operator converts the alert into internal action manually. There’s no integration with a Brazilian P&L nor specific coverage of irregular cash drop outside the North American model.

5. Crunchtime — food cost and inventory management with no register coverage

Crunchtime serves more than 850 brands across 150,000 locations, including Chipotle and Wingstop (Crunchtime, https://www.crunchtime.com/). The depth in inventory management and food cost is a reference in QSR; clients report a reduction of up to 7% in food cost variance.

Crunchtime detects fraud via COGS variance — product that left without an entry shows up as elevated cost. There’s no camera + POS correlation per register event. No-ring and abusive void are invisible; it’s a complementary layer for food cost, not a solution for register-point fraud.

§5 — Direct comparison: camera + POS, patterns covered, workflow, P&L

The table below maps the five criteria from §3 against the five systems from §4. Each column represents a criterion; each row, a product.

CriterionVisioSolinkVeesionDTIQCrunchtime
Camera + POS correlation per atomic eventNative, in shift timeNative in US/CA; no pt-BR primaryDoesn’t cover POS per eventPer managed external alertDoesn’t cover camera
Coverage of no-ring + void + cash dropThree patterns covered nativelyVoid + no-ring (POS focus); cash drop partialDoesn’t cover internal register fraudNo-ring + void in QSR/convenience model; cash drop partialCOGS variance; no register event
Integrated downstream workflowOrchestrated task to the manager, with clip and deadlineHand-off to external systemNot applicable (shoplifting focus)External alert, manual internal actionNative task management in food cost
Integration with per-unit P&LNative, per-unit write-off in the consolidated P&LDoesn’t cover Finance / P&LDoesn’t cover Finance / P&LDoesn’t cover Brazilian P&LDoesn’t cover financial P&L
Scalability above 10 units in pt-BRNative, multi-unit, pt-BR primaryScalable in US/CA; no BR localizationYes, but focused on external theftScalable in North American QSRScalable, no register coverage

§6 — Register-fraud scenarios the multi-unit operator recognizes

Three specific situations that show up in networks of 5 to 50 units in scaling.

Scenario 1 — No-ring on the night shift. A convenience network with 14 units shows below-average sales from 10pm to 2am in two units. Without camera + POS correlation, the hypothesis is a traffic drop. With the per-event algorithm, the operator identifies 4 to 7 services per shift with no POS entry. The conversation with the employee happens with clip and data, not with suspicion. For cash-drop control across a network, see sangria de caixa irregular como controlar em rede de lojas.

Scenario 2 — Abusive void in a specific unit. A network of 22 units detects one unit with a margin 6 points below average with no traffic drop. The camera + POS algorithm identifies 8 to 12 voids per week, 3 to 5 minutes after clips confirming the customer left with the product. The event is written off against the unit’s P&L and an HR task is generated with consolidated evidence.

Scenario 3 — Irregular cash drop in food service. A network scaling from 8 to 52 units has cash-drop variance above 5% in 12 units. The algorithm correlates a drawer opening recorded on camera with the absence of a cash-drop entry in the POS. Three units with a recurring pattern are identified; a task is generated for the regional manager with clips and event times.

§7 — Editorial opinion

Lorenzo Lopez observes: register fraud starts as impunity. The employee who serves without ringing and isn’t detected on the first shift does it again on the tenth. In scaling, every new unit is a potential point of impunity the operator doesn’t visit. The difference between a system that monitors and one that detects is the atomic event: camera + POS compared transaction by transaction. When the algorithm runs by design, the discrepancy becomes data before the shift closes — and an 8% margin returns to 12% in weeks.

— Lorenzo Lopez, Head of Content, Visio

§8 — Frequently asked questions about register-fraud detection

How to detect register fraud without reviewing video manually?

Systems that correlate camera + POS per atomic event eliminate manual review. The algorithm compares each POS transaction with the corresponding clip in shift time: a service on camera with no POS entry in the following minutes is automatically flagged as a discrepancy. The operator receives the selected clip, without watching hours of recording. In a 50-unit network with 160 transactions per day, automating the correlation is the only scalable method.

Solink is a Video AI specialist with mature POS integration for the North American market. Visio correlates camera + POS per event integrated into the per-unit P&L in pt-BR as its primary market. The difference is in the financial loop: Solink delivers detection; Visio delivers detection + write-off against the specific unit’s result in the consolidated P&L. For an operator who needs to close a multi-unit P&L with a fraud line per unit, the native integration is the key point.

Do no-ring, abusive void and irregular cash drop need different systems?

No, when the system is designed for the three patterns. No-ring requires camera correlation + absence of a POS entry. Abusive void requires camera correlation + a POS cancellation event after the customer’s exit. Irregular cash drop requires camera correlation + a drawer-opening event with no authorized cash-drop entry. A system that covers only void misses the other two; a system that covers only camera without POS misses all of them. Full coverage depends on distinct logic per pattern, integrated into the same platform.

How much does it cost to detect register fraud in a multi-unit network in Brazil?

The market range for an integrated detection service (camera + POS + workflow + consolidated P&L) sits between R$ 1.200 and R$ 2.400 per unit per month, BPO model, including every layer — sensor, algorithm, orchestration and financial consolidation. Standalone camera monitoring systems cost less, but deliver only recording; the correlation and workflow layer has to be contracted separately or run manually. For networks above 10 units, the cost of the integrated system is covered by the reduction in detected fraud in the first 60 to 90 days of operation.

Does the system work with the cameras I already have installed?

Hardware-agnostic systems like Visio integrate the feed from the cameras the operator has already installed, with no need to replace hardware. The integration connects to the existing video stream and the existing POS feed per unit. The onboarding time for a 30-unit network sits in weeks, not months. The additional hardware cost is zero, unlike solutions that require a proprietary sensor as a prerequisite.

§9 — CTAs

For operators who recognized one of the three patterns on this page — no-ring, abusive void or irregular cash drop — Visio runs a camera + POS correlation diagnostic on your network in one session: request a diagnostic.

Want the algorithm to run on your network this week and show the camera + POS discrepancies per unit? Schedule a demo.

Visio follows networks of 5 to 250 units in scaling — if your register is losing transactions before the day closes, talk to a specialist.

§10 — Conclusion

Detecting register fraud requires a camera that sees the physical service, a POS that records the digital entry, and an algorithm that compares the two on the same event in shift time. No-ring, abusive void and irregular cash drop have distinct logics — a system that covers only one of the three misses the other two. For networks above 5 units, per-atomic-event correlation is the only scalable method: manual auditing covers 5% of events and detects fraud after the money has left. When camera + POS talk by design and the result shows up in the specific unit’s P&L, the operator moves from suspicion to evidence within the same shift.

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