How Walmart Uses AI for Fraud Detection & Payment Security?

Walmart leverages artificial intelligence (AI) and machine learning (ML) extensively to enhance fraud detection and secure payments across its omnichannel operations, including in-store, online, and marketplace transactions. As a massive retailer processing billions in payments annually, Walmart focuses on real-time anomaly detection, behavioral analysis, and predictive modeling to minimize losses from fraud, such as stolen credit cards, account takeovers, and chargebacks. These systems analyze vast datasets—including transaction patterns, user behavior, device information, location data, and historical records—to flag suspicious activities proactively.

Key Applications in Payment Security and Fraud Prevention

• Real-Time Transaction Monitoring and Fraud Detection: Walmart employs ML models to scrutinize payment data in real-time, identifying patterns indicative of fraud like unusual transaction amounts, multiple attempts from different locations, or deviations from a customer's normal behavior. This includes preventing stolen credit card use, fake payment information, and account takeovers by analyzing login attempts, biometrics, and device fingerprints.
• Gift Card Scam Prevention: A proprietary system called Redemption uses algorithms to detect "red flag" markers for gift card fraud (e.g., rapid draining after loading). It automatically freezes suspicious balances, placing funds in escrow for victim recovery. Since 2018, this has helped return millions to scam victims, often targeted at seniors via impersonation schemes.
• E-Commerce and Marketplace Fraud: For Walmart.com and its third-party marketplace (offering over half a billion products), AI-driven real-time monitoring scans product listings for policy violations, intellectual property infringements, counterfeits, and fraudulent sellers. Machine learning, combined with automation and human oversight, helps remove bad actors and prevent misleading or unsafe items.
• Subscription and Omni-Channel Payments: In areas like Walmart's subscription services, multimodal AI fuses large language models (LLMs) with transaction logs and metadata to spot suspicious patterns in real-time, optimizing fraud prevention across online, mobile, and in-store channels.
• Broader Risk Mitigation: Walmart integrates AI into cybersecurity, using tools like Elasticsearch for ingesting traffic data (e.g., IP addresses, point-of-sale records) to detect scams in real-time. This extends to preventing chargebacks, friendly fraud, and internal threats.

Supporting Technologies and Outcomes

Walmart's approach combines rule-based systems with advanced ML models (e.g., neural networks for anomaly detection, logistic regression for classification). These reduce false positives, improve accuracy, and enable proactive interventions. Investments in AI have contributed to lower fraud losses, enhanced customer trust, and operational efficiency, aligning with industry trends where e-commerce fraud exceeds $40 billion globally annually.

While Walmart also explores blockchain for supply chain traceability (which indirectly aids anti-counterfeiting), its primary fraud and payment security relies on AI/ML for scalable, real-time protection. Ongoing advancements, including agentic AI for multi-step workflows, position Walmart as a leader in tech-powered retail security.