Retail fraud prevention: when a one size solution doesn’t fit all

Online merchants need a retail-specific fraud provider

In the fast-paced world of ecommerce, retail fraud prevention is a constant battle. Fraudsters have become increasingly sophisticated, leveraging advanced technologies like artificial intelligence to create more convincing fake identities and bypass traditional security measures. In 2021, chargebacks were estimated by Juniper Research to cost merchants a staggering $125 billion globally, with that number on track to rise to $206 billion by 2025. 

Many fraud prevention solutions offer a one-size-fits-all approach, using general patterns and rules to detect suspicious activities. While these can catch basic fraud attempts, they often fall short in the complex landscape of ecommerce. Why? Because they lack a crucial element: a deep understanding of retail-specific dynamics.

What retail-specific data is available to detect and prevent fraud?

To truly combat sophisticated fraud in ecommerce, while ensuring a smooth experience for legitimate customers, fraud prevention strategy needs to incorporate retail-specific insights. Let’s explore how a retail-specific provider would approach fraud prevention differently from a generic solution.

SKU-level data analysis

Effective fraud detection in retail requires deep SKU-level granularity and an understanding of diverse product categories. Fraud patterns often vary significantly between different types of products. For example, fraudsters often target specific high-value or easily re-sellable items such as exclusive launches. By analyzing patterns at the SKU level, a retail-specific model can identify suspicious ordering behavior that generic systems might miss.

Buyer patterns

Retailers encounter distinct buying behaviors influenced by seasonality, payment methods, demographics, etc. A generic solution applies the same risk rules to all customers based on general fraud indicators. For example, there has been an increased adoption of subscription payments in retail. When a retail-specific solution encounters subscription-type payments, it would ignore the absence of device ID and IP address while risk decisioning, whereas a general solution would deem such a transaction as risky and problematic. Understanding these patterns helps distinguish between a repeat customer and a potential fraudster.

Seasonality

Retail is cyclical. Holiday seasons, back-to-school periods and other seasonal events significantly impact shopping behaviors. A retail-focused fraud prevention system accounts for these fluctuations and incorporates seasonal trends into its risk assessment. For example, during holiday seasons when it is normal to expect an increase in sales for gift-related items, a retail-focused fraud prevention solution would take this seasonality trend into account, while also detecting out-of-season fraud attempts that a generic system might miss.

Event-driven shopper behavior

Retail fraud detection relies on identifying subtle behavioral anomalies that vary with the shopping context, such as rapid cart additions or using unusual payment methods during sales events. For example, retail-specific solutions deploy sophisticated anomaly detection and feedback loops to continuously improve their fraud protection. This helps the system recognize new patterns that emerge over time, both for legitimate shopping behavior and fraud attempts. Solutions not tailored to retail might miss these cues or apply one-size-fits-all rules that fail to capture the complexity of retail transactions.

Pre- and post-purchase risks

Retail-focused fraud prevention solutions offer a comprehensive approach that goes far beyond simple checkout fraud detection. Unlike general solutions, they address pre- and post-checkout retail fraud issues such as account takeover, reseller abuse, promo abuse, consumer abuse and returns abuse. By leveraging consortium models and retail-specific data, these solutions can identify risks even in challenging scenarios like guest checkouts, where a customer might appear new to the merchant but not to the model. This end-to-end strategy minimizes leakage throughout the entire customer journey, providing superior protection for retailers and enhancing the shopping experience for legitimate customers.

Partnership

Retail-focused fraud management solutions thrive on partnership, becoming an extension of the merchant’s team. They understand the unique aspects of each retailer’s business – from call centers and in-store kiosks to online-offline integrations and loyalty programs. This deep understanding allows them to anticipate how fraudsters might exploit specific policies or customer interactions across various channels. Whether it’s a digital-only retailer aiming for customer-friendly returns or a multichannel merchant balancing convenience with security, these partners tailor their approach accordingly. By aligning fraud prevention with each retailer’s specific user experiences and business goals, they create a cohesive strategy that not only protects against fraud but also drives conversions and enhances customer lifetime value.

How can retail data give merchants better defense against chargebacks?

False positives in retail fraud prevention are far more than minor setbacks—they’re significant threats to customer experience and long-term profitability. When a legitimate transaction is incorrectly declined, it doesn’t just cost a sale; it erodes trust and can permanently damage customer relationships. These negative experiences often ripple outward, potentially tarnishing brand reputation. When generic fraud prevention solutions lack retail-specific insights, the consequences for retail merchants can be severe:

Detecting missed fraud

Sophisticated fraudsters who understand retail patterns can slip through generic detection systems, leading to significant financial losses. For instance, they might exploit seasonal trends or target specific high-value SKUs in ways that generic systems aren’t equipped to detect. A compelling case in this area is Samsung’s partnership with Signifyd, where they successfully tackled previously undetected fraud. Within the first six months, Signifyd identified and mitigated several fraud trends, including Korean IP fraud, Dominican Republic reshipper schemes, and loan fraud through Samsung’s financing partner, TD Bank. This partnership resulted in a 35% reduction in average monthly liability from these loans, saving Samsung $3.9 million in just six months. Moreover, this was accomplished while also boosting order approval rates.

Reducing False positives

While missing fraudulent orders is undoubtedly costly, rejecting valid ones can be even more detrimental in the long run. Generic fraud solutions, lacking a nuanced understanding of retail’s unique patterns, often struggle with this delicate balance. They’re prone to both over-declining legitimate orders and letting sophisticated fraud slip through, creating a lose-lose scenario for retailers. To address this challenge, a leading ecommerce giant turned to Signifyd to eliminate shipping delays and manual reviews, boosting customer lifetime value by reducing false positives and enabling faster shipping. This partnership resulted in a 14% increase in approval rates and a 65% reduction in false positives, culminating in a $445 million annual topline impact while shifting liability for all fraud losses across their omnichannel experience.

Eliminating Inefficient manual reviews

Without contextual data, your fraud team may spend unnecessary time reviewing legitimate transactions, reducing overall efficiency. A retail-specific system provides rich context (e.g., product details, customer history, seasonal factors), allowing quicker, more accurate manual reviews when needed. A prime example of Signifyd’s impact is seen with Cuts Clothing, which fully outsourced their commerce protection to focus on growth. This shift allowed them to confidently expand into markets like Australia, Canada, the EU and Hong Kong. With an order approval rate over 99% and zero spend on chargebacks, Cuts Clothing saw a 122% ROI within six months. This freed up their team to focus on expanding their product line, a move that proved highly successful.

Generic fraud solutions can’t keep up with retail-specific fraud growth

To stay ahead of fraudsters, it’s crucial for retail merchants to implement fraud prevention solutions that understand the nuances of retail ecommerce. Look for fraud prevention solutions that have:

• Deployed machine learning and AI models based on data from a global consortium of retailers.
• Invested in automated, rapid model refreshes. This ensures new training patterns are quickly integrated into a model’s context, thus enabling models to stay ahead of emerging retail trends.
• Reporting and analytics that are real time and properly represent the retail buyer journey.
• Leadership with retail expertise. Their industry knowledge enhances risk assessment beyond general strategies. Understanding retail operations, trends and customer behavior allows for more accurate fraud detection, fewer false positives and improved customer satisfaction in ecommerce.
• The ability to show authority in retail both through customer stories demonstrating results and collaboration.

Remember, in the world of ecommerce fraud prevention, context is king. Generic solutions might catch the low-hanging fruit, but to truly protect your business and customers, you need a system that speaks the language of retail.

Want to learn more about retail-specific fraud protection? Let’s talk.