What Is Event-Driven Architecture? The Ecommerce Essential
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“Our ability to manufacture fraud now exceeds our ability to detect it.” – Al Pacino.
As the eCommerce industry expands, so does ecommerce frauds, resulting in huge losses. According to a report by Juniper Research, eCommerce businesses lose approximately $48 billion to fraud each year. Alarmingly, it is not just businesses bearing the brunt. About 43% of eCommerce customers have fallen victim to payment fraud, raising their concerns. While eCommerce businesses have security protocols, they fail to keep up with advanced fraud tactics. This is where AI steps in as a game-changer.
In this blog, we’ll discuss how eCommerce businesses can use AI to detect unusual activities, identify fraudulent patterns in real time, and protect businesses and customers from potential threats.
Some of the common types of eCommerce frauds businesses should be aware of include:
Criminals use stolen credit card information obtained through data breaches or phishing to make unauthorized purchases.
This takes place when a customer buys a product and then disputes the charge, claiming they didn’t receive the product or didn’t authorize the purchase. In 2023, there were over 238 million chargebacks globally, with 105 million coming from the US.
When a hacker obtains unauthorized access to a user’s account and purchases or steals personal information through phishing or exploiting data breaches. 29% of people have been affected by account takeover, i.e., 77 million adults.
This fraud involves three parties: the fraudster, a buyer, and a retailer. The fraudster creates a fake online store, gets buyers to place orders, and uses stolen credit card information to buy products from a real store, which is then shipped directly to the buyer.
This happens when people misuse promo codes or discounts, for example, reusing one-time coupons or creating fake accounts to get new customer deals.
“In this sense, it is a constant game of cat and mouse,” says Ole Matthiessen, Global Head of Cash Management Deutsche Bank. “No one is safe, with criminals targeting corporations of all sizes, across all industries.”
Let’s discuss how you can use AI to detect eCommerce frauds.
AI allows eCommerce platforms to monitor transactions as they happen and identify unusual behavior. For example, if multiple transactions are being made from the same IP address shortly or if the billing and shipping address don’t match, AI systems can flag them, helping businesses minimize risks and protect customer trust.
Using advanced algorithms, AI systems can analyze biometric data such as fingerprints, facial features, and voice patterns time, ensuring a quick and reliable identification. These systems learn from new data, adapt to changes in user behavior, and improve their ability to distinguish between legitimate users and potential threats.
User entity and behavior analytics (UEBA) uses machine learning and behavioral analytics to detect threats such as hacked firewalls, databases, servers, and malicious activities such as malware, ransomware, and DDoS attacks.
UEBA analyzes logs and alerts from data sources to understand normal behavior patterns for users. It uses machine learning to automatically detect unusual activities and flag them as a possible threat. Additionally, UEBA can assess sensitivity of specific assets and potential damage caused due to breach.
AI makes it easy to find unusual patterns in data that might indicate fraud. It collects and cleans data and further selects the attributes that help it to distinguish between normal and abnormal patterns. Next, it uses different modelling techniques such as supervised, unsupervised, and semi-supervised to build the model. Some common algorithms that are used in AI anomaly detection include Isolation Forest and K-means clustering.
Natural language processing (NLP) enables organizations to analyze and interpret large amounts of unstructured text data, such as messages and transaction notes. It uses techniques such as sentiment analysis and semantic analysis to identify patterns and unusual behaviours. For example, NLP models pull out important details such as names, dates, and amounts and flag any suspicious connections or errors that might indicate fake claims.
AI improves risk scoring and profiling by analyzing datasets to identify potential threats and vulnerabilities. Using machine learning, it processes historical data and transaction behaviors to create accurate risk scores compared to traditional methods.
This technique allows organizations to update and improve risk profiles as new data comes in, ensuring all decisions are taken based on latest information. For example, AI can analyze structured and unstructured data sources such as messages and news reports to build risk profiles. AI also helps reduce errors in risk assessments by considering extra details and context, making the evaluations more accurate.
eCommerce fraud is evolving continuously and demands innovative solutions to project from it. With AI, businesses can stay away from fraudsters by detecting suspicious activities and identifying unusual patterns. By implementing AI tools, businesses can ensure a safe shopping experience and protect their customers from potential harm. Ready to safeguard your eCommerce business with AI? Contact us for a free consultation.
