AI Loss Prevention & Fraud PRevention
AI-powered loss prevention solutions analyze video, transactions, returns, and behavioral patterns to identify potential loss as it happens. These tools help retailers respond faster while minimizing unnecessary friction for legitimate customers.
Everything You Need to Know About AI Loss and Fraud Prevention
AI-powered loss and fraud prevention technology uses artificial intelligence, computer vision, machine learning, and data analytics to identify suspicious activity, detect potential theft and fraud, and help retailers reduce losses across stores, checkout, e-commerce, and other customer transactions. AI can analyze transactions and behaviors, identify unusual patterns, flag high-risk activity, detect checkout errors or theft, and alert employees or loss prevention teams when intervention may be needed.
For retailers, the goal isn't simply to catch more theft or fraud. The real opportunity is to create a smarter, more proactive loss prevention strategy that identifies risks earlier, reduces shrink, improves investigation efficiency, limits false positives, protects employees and customers, and helps retailers prevent losses while maintaining a positive shopping experience.
Questions to Ask Before Choosing a Provider
1.How effective is the AI at reducing loss and fraud?
How does the AI improve loss and fraud detection compared with our current process?
How is effectiveness measured, and what results have similar retailers achieved in reducing shrink, theft, fraud, and investigation time?
2. Will it integrate with our technology?
Does it integrate with our existing POS, self-checkout, e-commerce, inventory, payment, video surveillance, and loss prevention systems? What additional hardware, integrations, or technology will be required?
3. What data does the AI use?
What historical and real-time data is required for the system to work effectively?
Can it analyze transactions, video, inventory, returns, payments, employee activity, customer behavior, and other data sources?
4. What types of loss and fraud can it detect?
Can the AI identify shoplifting, missed scans, product switching, return fraud, employee theft, payment fraud, organized retail crime, and other suspicious activity?
How does detection differ across stores and digital channels?
5. How accurate are alerts and how are they handled?
How does the system distinguish suspicious activity from legitimate behavior and minimize false positives?
How are incidents prioritized, and when should employees or loss prevention teams review, investigate, or intervene?
6. What is the business case and ROI?
What is the total cost, including software, hardware, implementation, integration, training, and ongoing support?
What measurable improvements should we expect in shrink, fraud losses, detection rates, investigation time, and employee productivity?
7. Can it reliably scale across our operation?
Can the platform support our number of stores, transactions, cameras, employees, markets, and sales channels?
How long does implementation typically take from pilot to full rollout, and what resources will our team need to provide?
Where AI Can improve Loss and Fraud Prevention
1. Theft Detection
AI can analyze transactions, video, product movement, and customer behavior to identify potential theft, including missed scans, product switching, concealment, and other suspicious activity.
2. Checkout Loss Prevention
AI can monitor self-checkout and staffed checkout activity to identify scanning errors, skipped items, incorrect product entries, unusual transactions, and other behaviors that may contribute to shrink.
3. Return & Refund Fraud
AI can analyze return patterns, purchase history, receipts, customer behavior, and transaction data to identify suspicious returns, refund abuse, and other potentially fraudulent activity.
4. Employee Fraud & Internal Theft
AI can identify unusual employee activity involving discounts, voids, refunds, overrides, transactions, inventory, and other behaviors that may indicate internal theft or fraud.
5. Payment & E-Commerce Fraud
AI can analyze transactions, payment behavior, account activity, order patterns, and other risk signals to identify potentially fraudulent purchases, account abuse, and suspicious online activity.
6. Organized Retail Crime Detection
AI can connect incidents, transactions, locations, products, individuals, and behavioral patterns to help retailers identify coordinated theft and organized retail crime activity across multiple stores and channels.
7. Loss Prevention Analytics & Investigations
Retailers can use AI to prioritize alerts, connect transaction and video evidence, identify trends, manage investigations, and analyze loss patterns to help teams focus resources on the highest-risk activity.
Key AI Loss and Fraud Prevention Challenges
1. Data Quality & Accuracy
AI loss prevention systems depend on accurate transaction, inventory, video, customer, employee, and incident data. Incomplete or inaccurate data can lead to missed threats or incorrect alerts.
2. System Integration
The AI must connect effectively with existing POS, self-checkout, e-commerce, inventory, payment, video surveillance, and loss prevention systems to create a complete view of potentially suspicious activity.
3. False Positives & Detection Accuracy
AI may incorrectly identify legitimate customer or employee behavior as suspicious. Too many false alerts can waste employee time, reduce trust in the technology, and negatively impact the customer experience.
4. Privacy & Customer Concerns
Technologies involving cameras, computer vision, customer data, or behavioral analysis can raise privacy concerns. Retailers need to ensure data is collected, stored, and used appropriately while maintaining customer trust.
5. Changing Theft & Fraud Tactics
Shoplifting, organized retail crime, payment fraud, return fraud, and other threats continue to evolve. AI systems need to adapt as criminals change their methods and identify new ways to avoid detection.
6. Automation vs. Human Oversight
Not every suspicious event should result in automatic action. Retailers need to determine when AI should generate an alert and when employees or loss prevention teams should review, investigate, or intervene.
7. Employee Adoption & Change Management
Loss prevention teams, store associates, managers, and other employees need to understand how the technology works, how to respond to alerts, and how their responsibilities change as AI becomes more involved in loss prevention.
How AI Loss and Fraud Prevention Works
1. Data is Collected
AI connects data from POS systems, self-checkout, e-commerce, cameras, inventory platforms, loyalty programs, returns, payments, and other systems to create a more complete view of transactions and potential loss.
2. Transactions & Behaviors Are Analyzed
AI analyzes transactions, customer and employee activity, product movement, checkout behavior, returns, discounts, voids, and other events to identify patterns that may indicate theft, fraud, errors, or suspicious activity.
3. Normal Patterns Are Established
The system learns typical transaction and behavioral patterns across stores, employees, customers, products, and channels to establish what normal activity looks like and help identify unusual behavior.
4. Suspicious Activity is Identified
AI identifies potential risks such as missed scans, product switching, unusual returns, excessive discounts or voids, suspicious payment activity, organized retail crime patterns, and other behaviors associated with loss or fraud.
5. Risk is Assessed & Prioritized
The system evaluates suspicious activity based on risk, frequency, value, historical patterns, and other factors to prioritize the events most likely to require attention and reduce unnecessary alerts.
6. Teams Are Alerted & Investigate
Loss prevention teams and store employees can receive alerts, review transactions, video, and supporting data, investigate incidents, and determine when intervention or additional action may be necessary.
7. AI Continues Monitoring & Learning
The system continuously monitors activity and learns from investigations, confirmed incidents, false positives, transaction patterns, and emerging fraud techniques to improve detection accuracy and help retailers prevent future losses.
Featured Provider:
Everseen
Everseen, is a global retail technology company specializing in computer vision and AI-powered loss prevention. Its technology monitors activity across self-checkout, staffed checkout lanes, and store aisles to identify missed scans, suspicious behavior, and operational errors in real time.
Everseen’s flagship solution, Evercheck, can prompt customers to correct scanning mistakes or alert store associates when assistance is needed. The company says Evercheck analyzes more than 30 loss-related behavior patterns and has been deployed across more than 150,000 self-checkouts worldwide. Everseen also offers technology for detecting shelf-level theft, monitoring shopping carts, recognizing produce, and analyzing broader store activity.
Top Benefits
🛡️ Reduce Retail Shrink
Identify missed scans, product switching, suspicious transactions, and other sources of loss before they significantly affect store profitability.
👁️ Detect Loss in Real Time
Computer vision continuously analyzes checkout and store activity, allowing retailers to respond while an event is still happening.
⚡ Enable Faster Intervention
Automatically prompt customers to correct honest mistakes or alert associates when additional assistance or review may be required.
🛒 Protect Every Checkout
Monitor self-checkout stations, staffed registers, kiosks, produce transactions, and items left inside or underneath shopping carts.
📊 Identify Loss Patterns
Turn checkout and store activity into actionable insights that reveal recurring issues involving products, locations, transactions, and operating procedures.
������ Preserve the Customer Experience
Use more targeted alerts to reduce unnecessary interventions and help legitimate customers complete purchases with less disruption.
Existing Customers
Target Verticals
Other Leading Loss and Fraud Prevention AI Providers
Retailers can also consider AI loss prevention providers specializing in organized retail crime intelligence, return fraud, policy abuse, and ecommerce transaction protection.
Auror
Auror uses retail crime intelligence to connect incidents, identify repeat offenders, uncover organized crime patterns, and improve collaboration between retailers and law enforcement—helping reduce theft, strengthen investigations, and create safer stores.
Appriss
Appriss provides AI-powered solutions for returns, claims, transaction risk, and retail loss analytics. Its technology evaluates customer behavior in real time to help retailers identify fraudulent or abusive returns while continuing to provide a convenient experience for legitimate shoppers.
Riskified
Riskified helps ecommerce retailers detect payment fraud, account abuse, chargeback risk, and misuse of customer-friendly policies. Its machine-learning platform analyzes digital transactions and customer behavior to help merchants approve legitimate purchases while stopping suspicious activity.
