A Hybrid Model: The Game-Changer in eCommerce Fraud Detection
The digital marketplace’s growth has been nothing short of exponential, but it’s a double-edged sword. While eCommerce is a boon to consumers and businesses alike, it also creates an environment ripe for fraudulent activities. Online payment fraud alone is projected to exceed $343 billion globally by 2027, according to Juniper Research. One of the most challenging fraud types to detect, friendly fraud, is a significant contributor to these losses.
Friendly Fraud: A Hidden Threat
Friendly fraud occurs when a consumer makes an online shopping purchase with their own credit card and after receiving the merchandise disputes the charge with their credit card company . Mostly they claim that the purchased goods were never delivered, or the card was used without their consent, effectively “shoplifting” the item.
It’s called “friendly” fraud because it often looks just like ordinary, legitimate transactions. The credit card is not stolen; it belongs to the person making the purchase. However, the chargeback is fraudulent, causing a significant problem for businesses. According to a report by Aite Group, friendly fraud has been growing at a rate of 41% every two years and costs ecommerce retailers billions of dollars annually.
The Integration of AI with a Rule Engine
To combat this multifaceted threat, many eCommerce businesses are turning to innovative solutions, notably integrating AI with a rule engine and employing post-payment analysis.
AI and machine learning algorithms are incredibly adept at scanning vast amounts of data to identify patterns and potential fraud instances. They can adapt to changing behaviors and reduce the likelihood of false positives. Simultaneously, the rule engine applies a set of pre-established policies to filter out potentially fraudulent transactions based on known fraud indicators.
The combination of AI with a rule engine provides a balanced and efficient approach to fraud detection. The AI’s adaptive capabilities are complemented by the rule engine’s consistency, creating a safety net that can catch diverse fraud instances.
The Role of Post-Payment Analysis
Post-payment analysis is another crucial component in this fight against fraud. Post-payment analysis means continuing to examine transaction data even after the transaction has been completed. This practice helps identify any suspicious activity that may not have been apparent during the transaction process.
This layer of scrutiny is particularly valuable when dealing with friendly fraud. For instance, if a customer frequently files chargebacks for transactions completed from the same IP address or device, it raises a red flag. The system can identify this pattern and alert the business, even if each individual transaction seemed legitimate at the time.
The integration of AI with a rule engine and post-payment analysis provides a comprehensive and adaptive system that learns from each transaction, continuously refining its fraud detection capabilities. Implementing this approach can help mitigate the risks associated with fraudulent transactions, including friendly fraud, which costs eCommerce businesses billions of dollars annually.
As we continue to navigate the rapidly evolving digital landscape, it’s clear that these advanced, AI-powered systems are not just a luxury; they’re a necessity. They provide an invaluable line of defense against the ever-growing threat of fraud, protecting businesses and consumers alike.