Survey data reveals that theft cost retailers over $121 billion in gross revenue last year, and experts predict that number will grow to more than $150 billion by 2026. Moreover, retailers reported a 93% uptick in the number of shoplifting incidents and a 90% increase in uncollected revenue over the previous five-year period. These findings underscore the magnitude of losses the retail industry has already absorbed and the strategic imperative to address this problem decisively.
In the traditional retail model, losses came in predictable forms, such as shoplifting, employee theft, process failures, administrative errors, and inventory spoilage. Today, bad actors are using AI to identify system vulnerabilities, bypass security measures, and launch attacks that can result in devastating consequences.
For example, AI-enabled bots are scraping customer data, testing stolen credentials, and crashing websites. Cybercriminals are using AI to create fake online personas to commit fraud at scale. Malicious actors are tampering with the supply chain by infiltrating billing systems and communications to generate sham invoices and re-route shipments. Hackers are injecting malicious code into website payment forms to steal credit card data and personal information.
You can’t prevent these types of attacks using old school technologies because perpetrators can easily exploit the rigidity, limited adaptability, and lengthy response times inherent in this approach.
To address this issue, IHL lays out a four-part framework for AI-powered retail analytics, starting with descriptive (what happened), diagnostic (why it happened), predictive (what could happen), and prescriptive (what should happen next). The research shows that retailers who leverage predictive and prescriptive analytics are significantly outperforming competitors using legacy methods for loss prevention.
Implementing AI-Powered Retail Analytics
When it comes to retail loss prevention, AI-powered retail analytics help you move beyond reactive strategies and implement proactive and predictive approaches that deliver better outcomes.
AI seamlessly integrates data from a wide range of sources, such as point-of-sale (POS) systems, video surveillance, inventory management, employee access logs, external data feeds, and more. Because you can visualize the interaction of these factors together, you achieve a holistic view of your operations and enable data-driven decision-making.
AI-powered solutions can continuously monitor transactions and activities, flagging suspicious behavior in real time. Because your retail analytics tools can catch and remediate theft as it occurs, you can prevent losses before they escalate.
Using AI-powered pattern recognition, you can analyze transactional data to surface fraud. With an algorithm that can handle vast amounts of data, your solution can detect complex schemes involving multiple accounts and transactions. For individual customers, you can establish baseline patterns and automate alerts when deviations arise, such as unusual spending or other anomalies.
Predictive analytics analyze historical data to identify patterns and predict potential risks, empowering you to anticipate and prevent losses before they happen. For example, say your solution detects a pattern that shows a 30% increase in inventory shrinkage in stores with a high volume of temporary staff during the holiday season. Because the technology can identify specific stores and timeframes that fit this pattern, you can deploy targeted security measures to reduce losses proactively.
Leveraging Generative AI
Generative AI refers to a type of artificial intelligence that generates new content based on data inputs and uses machine learning to analyze patterns and generate realistic outputs. These models are trained on various data types, such as audio, visuals, and text, and learn to interpret patterns in the data. When prompted, generative AI models can use their knowledge to supply new content.
The technology integrates with existing retail security measures in numerous ways to advance their capabilities and effectiveness. For example, generative AI improves existing closed-circuit television systems by augmenting video analytics capabilities, enabling you to detect unusual movements, identify potential shoplifters, and catch known offenders in real time. It also integrates with biometric security measures, such as facial recognition, to ensure only authorized personnel can access restricted areas, minimizing the risk of internal theft.
For inventory management, generative AI analyzes data from POS systems to reveal suspicious transaction patterns that indicate fraud at checkout. The technology also integrates with tracking systems to establish real-time visibility into stock levels, preventing losses related to understocking or overstocking.
Generative AI can integrate with existing hardware, such as door, window, and motion sensors, to improve intrusion detection systems, and user interface enhancements make these tools more responsive and intuitive. Leveraging these capabilities, you can create a more efficient and effective loss prevention strategy that safeguards assets and improves overall security.
Executing AI for Retail Loss Prevention
When you’re ready to propose AI-powered loss prevention solutions, start by defining the objectives and the details of the use cases that apply to your business.
Next, assess your current infrastructure, which involves evaluating your existing security systems. You’ll also need to identify potential integration points for AI technologies, such as access control management, supply chain management, or customer behavior analytics.
Once you’ve completed your assessment, collect and prepare your data—this entails gathering historical information (e.g., security footage, transactions, and inventory). The quality and completeness of your data sets will directly impact performance and compliance with regulatory requirements, so it’s important to prioritize this step.
When selecting a solution that best aligns with your use cases, consider both traditional and generative AI models (e.g., predictive analytics for fraud detection or generative AI for pattern recognition). From there, adapt and train your chosen models using historical data to improve accuracy.
To integrate your models with existing systems, develop APIs or plug-ins to incorporate AI into current applications and ensure seamless integration with key systems, such as CCTV, POS, and inventory systems. Then, implement real-time monitoring, which involves continuous analysis of data streams and configuring alerts for suspicious activities.
Establish security measures, which require implementing robust data protection protocols and setting up access controls and user authentication for AI tools. Next, test and validate by conducting thorough trials in a controlled environment and evaluating AI performance against established benchmarks.
When you’re confident and ready, it’s time to train the staff. This involves educating employees on new AI-enhanced loss prevention measures and rolling out the system in phases, starting with pilot launches. You’ll need to continuously assess your AI system performance and refine models based on emerging threat patterns and new data.
To ensure compliance and ethical use, review your AI usage regularly against legal and ethical standards, and update policies to address evolving AI capabilities and regulations. Following this methodology, you can succeed in implementing AI and generative AI for loss prevention while reducing shrinkage and improving operational efficiency.
Ram Venkataraman is the CEO of Kibo, where he leverages over 25 years of experience in the software industry to drive the company’s growth and success. His leadership philosophy centers on nurturing individual and team well-being while passionately serving employees, customers, and partners. Ram’s career encompasses a broad spectrum of roles, from guiding bootstrapped startups to steering functions in public companies. Prior to his tenure at Kibo, he was the CTO of NCR payment platforms, demonstrating his deep expertise in technology and product development.