Many people are embracing the convenience of online shopping, leading to a surge in ecommerce transactions. However, amidst this growth, there’s a hidden danger: the risk of fraud.
Scammers are always looking for weaknesses in your online store to exploit them. In fact, US ecommerce businesses are expected to lose a staggering $48 billion to payment fraud in 2024, according to Statista.
That’s why staying vigilant is no longer enough. You need to tap into the cutting-edge potential of Artificial Intelligence (AI) for ecommerce fraud detection and AI-Driven Customer Support.
In this article, let’s explore how AI can detect and prevent different types of ecommerce fraud and explore software solutions you can use to strengthen your business.
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Ecommerce fraud is any illegal activity that exploits online businesses and causes financial loss, damaged reputation, or customer distrust.
According to Statista, 62% of small and medium-sized businesses have reported an increase in ecommerce fraud attempts since the start of the pandemic.
Fraudsters use various tactics to carry out malicious activities, such as identity theft, stolen credit cards, account takeovers, and fake orders.
To tackle this growing problem, leveraging AI-powered fraud detection systems has emerged as a game-changer for ecommerce store owners.
Before we get into how you can use AI for ecommerce fraud detection, let’s first learn how scammers steal money from your customers and you.
Among the numerous online fraud tactics targeting ecommerce platforms, here are the top eight threats you need to be aware of:
Credit card fraud is a common form of ecommerce fraud, often carried out by amateur fraudsters.
It involves acquiring and using stolen credit card information to make unauthorized purchases online.
The fraudster usually employs tactics like reshipping goods and masking their identity with techniques such as residential proxies.
Card testing is a popular tactic fraudsters use to verify the validity and spending limit of stolen credit cards.
They make low-value purchases to test if the card is still active and then proceed to make larger purchases.
Chargeback fraud occurs when a scammer makes a purchase, receives the item, and then initiates a chargeback. It results in the online store owner paying for the transaction while the customer, who’s the scammer, gets a refund.
This type of fraud, also known as “friendly fraud,” can be challenging to detect as it often involves seemingly genuine claims.
Account takeover fraud is when a scammer gains unauthorized access to a customer’s account on an ecommerce store and exploits it to make fraudulent purchases.
Scammers commit this type of fraud using different methods, such as:
Return fraud is when individuals exploit a retailer’s return policy by sending back items not eligible for return.
These items can be stolen merchandise, used products, items from different retailers, or counterfeit goods.
Gift card fraud involves fraudsters purchasing gift cards using stolen payment information. They then either use these gift cards themselves or sell them to other unsuspecting individuals.
Some might even pose as government representatives to deceive customers into loading money onto their cards.
Triangulation fraud is when a fraudster sets up a fake online store that lures customers with low prices on popular products.
The fraudster then steals the customer’s credit card information to buy the requested goods from a legitimate ecommerce store. Unfortunately, the customers end up paying the regular price and unknowingly have their credit card details stolen.
Interception fraud occurs when fraudsters use stolen credit cards to place orders on your ecommerce store and intercept the package for themselves.
They use tricks like changing the shipping address with customer service, rerouting packages, or physically intercepting deliveries.
Now let’s explore how AI detects ecommerce fraud. You’ll discover two machine learning approaches that identify vulnerabilities in your online store and predict potential threats.
AI detects ecommerce fraud by categorizing transactions into normal and potentially fraudulent based on their deviation from the expected patterns.
Various data variables, such as transaction details and images, are analyzed to assess the legitimacy of user actions and identify inconsistencies in the provided information.
Anomaly detection is a straightforward approach that offers binary answers, making it useful in certain situations.
For example, if the AI finds a transaction suspicious, it can notify you for further investigation or prompt the customer to undergo additional verification steps.
It typically relies on supervised machine learning, which involves training an algorithm with labeled historical data to predict target variables in future data.
Besides detecting obvious abnormal online behavior, AI fraud systems can also recognize emerging fraud patterns.
AI systems can adapt to new behavior patterns, such as changes in purchase categories during unique events like the pandemic.
For example, during the pandemic, individuals who had never previously engaged in home improvement or home fitness purchases were making unexpected transactions in these categories.
AI can help by adapting to these shifts in real time and preventing false declines.
This type of complex learning process requires an advanced fraud detection AI system called unsupervised machine learning.
Unsupervised machine learning involves analyzing unlabeled data to uncover hidden patterns and relationships.
This AI system not only autonomously discovers and classifies unknown patterns but also differentiates between the types of fraudulent activity.
As fraud becomes a bigger challenge in ecommerce, using AI for fraud detection offers significant benefits.
Here are some of them:
Although AI has the potential to revolutionize ecommerce fraud detection, you should be aware of potential challenges and limitations.
Let’s explore top-rated AI fraud prevention software for ecommerce businesses based on aggregated ratings and reviews from reputable sources such as G2 and TrustRadius.
Signifyd is a leading fraud prevention product that offers end-to-end commerce protection. It was recently named one of the ten most innovative AI companies in the world by Fast Company.
Signifyd integrates with Magento, Shopify, BigCommerce, Salesforce Commerce Cloud, ReCharge, Inai, Primer, and Fraud.net.
Features:
Pros:
Cons:
Kount is an AI-driven identity trust software that leverages its extensive global network and billions of trust and fraud signals to protect online stores and payment processors.
It integrates with Shopify, BigCommerce, Magento, Dodgeball, Salesforce Commerce Cloud, Shift4Shop, and Miva.
Features:
Pros:
Cons:
SEON is a comprehensive fraud detection platform that empowers ecommerce stores of all sizes to gain complete control over their accounts, interactions, orders, and transactions.
SEON offers seamless integration with Shopify through its dedicated app, and provides integrations with various third-party systems through its API. Plus, you can expand integration capabilities by connecting through Zapier.
Features:
Pros:
Cons:
Some of the best ecommerce fraud prevention tools that didn’t make it to the list but deserve recognition are as follows:
To stay ahead of fraudsters, it’s crucial to be alert and proactive in identifying signs of ecommerce fraud.
When you recognize these red flags, you can effectively strengthen your defenses and protect your operations.
When a customer has a previous purchase history with your business, you should monitor them for any unusual activities originating from different than usual locations.
For instance, if the customer usually makes purchases from an IP address in the US but suddenly starts purchasing from China, that might indicate that the transaction could be fraudulent.
Fast, consecutive transactions or unusually high order volumes can indicate ecommerce fraud.
While it is possible for a genuine customer to make multiple back-to-back purchases, it most probably indicates fraudulent activity, such as card testing on your online store.
Multiple orders from numerous credit cards of a single user account or consecutive back-to-back orders from different accounts with shared characteristics (e.g., same IP address) indicate potential cases of stolen credit card fraud or card testing fraud.
Honest customers may occasionally forget their PIN or exceed a card’s limit unknowingly.
However, it helps to be cautious if an account attempts over three to five unsuccessful transactions with incorrect credentials, including the card number, CVV, expiry date, and name.
Multiple shipping addresses for orders placed using a single credit card may signal potential fraud. But it’s important to consider that the buyer could be a dropshipper.
Nevertheless, conducting dropshipping without a valid contract can be unethical and potentially illegal if your company prohibits this practice.
In the ever-evolving world of ecommerce, the importance of fraud detection and prevention cannot be overstated for both online sellers and consumers.
And as the battle against fraud intensifies, artificial intelligence emerges as the most viable solution to tackle this challenge head-on.
While fraud can never be wholly eliminated, AI empowers you with quick and accurate data processing at scale. Looking ahead, I believe AI will continue to play a pivotal role in fraud prevention.
In this article, I’ve covered different fraud detection tools to help you safeguard your online store, protect your customers, and outsmart those sneaky fraudsters.
Even if you’re not yet ready to implement AI, you now recognize the telltale signs of ecommerce fraud.
So why wait? Start fortifying your business today.
Niyotee Khedekar is a B2B ecommerce writer who loves writing about everything ecommerce, from the developments in Amazon, Magento, Shopify, and WordPress, to AI advancements and more. When she’s not writing, you can find her walking her dogs and playing chess.
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