How AI is Used to Detect Fraud in Retail?

Imagine this: a seemingly ordinary online transaction for a high-priced item, say, a state-of-the-art television, takes place on a bustling e-commerce platform. The customer's payment processes smoothly, and the order is confirmed. But behind the scenes, a silent alarm is triggered.

The AI-powered fraud detection system has identified several red flags: the order originated from an IP address known for suspicious activity, the billing and shipping addresses don't match, and the purchase amount is significantly higher than the customer's average spending pattern.

Before the fraudster can even celebrate their ill-gotten gains, the system takes action. The transaction is immediately flagged, and the order is put on hold for further review. The retailer's fraud prevention team investigates, confirming the fraudulent activity and preventing a costly loss for both the business and the unsuspecting customer.

This scenario highlights the growing threat of fraud in the retail industry and the power of AI to combat it. From sophisticated shoplifting schemes in brick-and-mortar stores to complex payment fraud in the digital realm, retailers face a constant battle against those seeking to exploit vulnerabilities and steal valuable assets.

The Rise of AI-Powered Loss Prevention

The good news is that AI is revolutionizing retail loss prevention by enabling businesses to take a proactive and predictive approach to security. AI-powered systems are used to analyze large datasets, identify patterns, and detect anomalies that may indicate fraudulent activities.

These systems can track purchasing behaviors, analyze stock movements, and identify discrepancies in real-time. Additionally, AI can forecast when and where loss is likely to occur, allowing retailers to allocate resources effectively.

How AI is Used to Detect and Prevent Loss?

AI-powered systems are adept at analyzing vast amounts of data, uncovering hidden patterns and insights that humans might miss. Here are some ways AI is used to detect and prevent retail loss:

• Shoplifting and Organized Retail Crime: AI-powered video analytics can automatically detect and flag potentially suspicious activities, such as shoplifting, organized retail crime, and employee theft, in real-time. This allows security personnel to take immediate action, preventing losses and ensuring a safer shopping environment. For instance, AI can analyze video footage to identify individuals loitering in high-risk areas, repeatedly entering and exiting the store, or exhibiting suspicious behavior like hiding merchandise.

• Payment Fraud: Fraudsters may employ various tactics, including card skimming, phishing, or intercepting online payment data to compromise customer credit card information and make unauthorized purchases. AI algorithms can analyze transaction patterns, detect anomalies indicative of fraudulent activity, and prevent unauthorized transactions before they occur. For example, if a customer who typically makes small purchases suddenly attempts to buy a high-value item with a different shipping address, AI can flag the transaction as potentially fraudulent.

• Inventory Shrinkage: Inventory shrinkage, which includes losses from theft, damage, and administrative errors, costs retailers billions of dollars each year. A study by the National Retail Federation found that shrink accounted for an average of 1.44% of total retail sales in 2022. AI can help retailers optimize inventory management by analyzing historical sales data, identifying trends, and predicting future demand. This helps prevent overstocking and stockouts, reducing losses due to spoilage, obsolescence, or storage costs. For example, AI can predict demand for seasonal items, ensuring that stores have the right amount of stock on hand to meet customer needs without excess inventory that could lead to losses.

• Employee Theft and Fraud: Employee theft is a significant contributor to retail shrink, accounting for an estimated 35.8% of losses, according to the 2022 National Retail Security Survey. AI can play a crucial role in monitoring employee behavior, detecting suspicious activities, and identifying potential fraud. By analyzing data from point-of-sale systems, access logs, and performance records, AI algorithms can pinpoint anomalies and patterns that suggest fraudulent behavior, enabling retailers to take action such as internal investigations, stricter access controls, or ethics training. For example, AI can identify employees who frequently void transactions or override discounts, which could be a sign of fraudulent activity.

Top AI-Powered Retail Loss Prevention Solutions

Some of the top AI-powered retail loss prevention solutions include:

• Appriss Retail: Provides retailers with a comprehensive suite of solutions to protect against fraud and abuse across all channels, including point-of-sale, e-commerce, and mobile. Their solutions leverage AI to analyze transactions, identify suspicious patterns, and prevent fraud in real-time.

• Dragonfruit AI: Offers analytics apps that deliver customizable insights into various aspects of retail operations, including loss prevention, customer analytics, and operational efficiency. Their AI-powered platform helps retailers identify trends, predict future outcomes, and optimize their operations for maximum profitability.

• Everseen: Utilizes a Visual AI platform incorporating high-resolution cameras, computer vision, and AI to detect and prevent losses, especially at self-checkout kiosks. Their system analyzes customer behavior in real-time, identifying potentially fraudulent activities such as scan avoidance, sweethearting, and incorrect weight declaration.

• Riskified: Uses AI to analyze comprehensive data, including device data, behavioral insights, and past chargebacks, to accurately identify fraudulent transactions. Their solution helps retailers reduce chargebacks, increase approval rates, and improve customer experience by automating fraud detection and prevention.Solink: Offers a cloud-based video surveillance solution that integrates with existing hardware and provides advanced analytics to improve security and customer conversion analysis. Their platform allows retailers to monitor their stores remotely, identify suspicious activities, and gain insights into customer behavior to optimize store layout and merchandising.

The Future of AI in Retail Loss Prevention

The future of AI in retail loss prevention looks promising, with emerging trends such as behavioral analysis and predictive modeling expected to further enhance loss prevention efforts. AI is set to play an even greater role in identifying repeat offenders, predicting potential threats, and proactively preventing losses before they occur.

AI can analyze historical data, identify trends, and predict future losses, enabling proactive intervention and prevention strategies. This allows retailers to allocate resources effectively, focus on high-risk areas, and implement preventive measures to mitigate potential threats.

Emerging trends in AI-powered loss prevention include:

• Personalized Loss Prevention: AI can be used to create personalized loss prevention plans for each customer, taking into account their individual purchasing behavior, risk profile, and preferences.

• Integration with other technologies: AI can be integrated with other emerging technologies, such as blockchain and IoT, to enhance security and improve loss prevention efforts. For example, blockchain can be used to create a secure and transparent record of transactions, while IoT sensors can provide real-time data on inventory levels and customer movements.

• Enhanced accuracy and efficiency: As AI algorithms become more sophisticated, they will be able to identify and prevent losses with even greater accuracy and efficiency. This will help retailers reduce shrink, improve profitability, and create a safer shopping environment for their customers.

How to Implement AI in Retail Loss Prevention?

Implementing AI in retail loss prevention requires a strategic approach, including:

• Identifying specific needs and challenges: Retailers need to clearly define their loss prevention goals and identify the areas where AI can be most effective.

• Collecting and Integrating Data: AI systems rely on high-quality data, so retailers need to ensure they have the necessary data infrastructure in place.

• Choosing the Right AI Solutions: Retailers should carefully evaluate different AI solutions and choose the ones that best meet their needs and budget.

• Ensuring Ethical Considerations and Privacy Safeguards: Retailers must be mindful of the ethical implications of using AI and implement appropriate safeguards to protect customer privacy. This includes adhering to regulations like GDPR and being transparent about data usage.

Case studies of retailers using AI for loss prevention

Several retailers are successfully using AI for loss prevention, including:

• Kroger: Uses Everseen's Visual AI platform to reduce shrink at self-checkout kiosks, resulting in a significant decrease in losses. Kroger reported a 35% reduction in self-checkout losses after implementing Everseen's solution.

• Wayfair: Leverages Riskified's AI-powered solution to accurately identify fraudulent transactions, thereby reducing chargeback rates by an impressive margin. Wayfair saw a 23% reduction in chargebacks and a 10% increase in approval rates after implementing Riskified.

• Canadian Tire: Employs Solink's cloud-based video surveillance to improve security and gain insights into customer behavior, leading to enhanced operational efficiency. Canadian Tire reported a 15% reduction in theft and a 5% increase in customer conversion rates after implementing Solink.

Challenges of Using AI in Retail Loss Prevention

Despite its potential, there are challenges associated with using AI in retail loss prevention, such as:

• Potential for Bias: AI systems can be biased if the training data reflects historical prejudices. Mitigating bias through diverse datasets is essential.

• Lack of Transparency: AI systems often function as "black boxes," making it difficult to understand their decision-making processes. Transparency initiatives can help address this.

• Privacy Concerns: The use of AI for surveillance and data analysis raises privacy concerns that need addressing, calling for strong data protection measures and customer trust initiatives.

Ethical considerations for AI-powered retail loss prevention

Retailers need to ensure that their use of AI for loss prevention is ethical and respects customer privacy. This includes being transparent about how AI is being used, obtaining consent for data collection, and ensuring that AI systems are not used in a discriminatory manner.

The Future of Retail Loss Prevention is Here

Loss prevention is a critical aspect of running a successful retail business. By understanding the current state of retail loss prevention and embracing innovative technologies like AI, retailers can protect their assets, reduce financial losses, and provide a safe shopping environment for their customers.

Protecting Your Business and Your Customers with MicroStrategy

MicroStrategy provides a comprehensive platform for retailers to implement AI-powered loss prevention solutions. With MicroStrategy ONE, retailers can:

• Analyze data from various sources to identify suspicious patterns and anomalies.

• Predict and prevent potential losses before they occur.

• Optimize inventory management and enhance video surveillance.

• Monitor employee behavior and detect potential fraud.

• Ensure ethical considerations and privacy safeguards.

Contact us today to learn more about how we can help you protect your business and your customers. Our trusted AI tool offers scalable, secure options that modernize legacy systems while prioritizing long-term data security and privacy.