The number and variety of e-commerce fraud attacks is constantly increasing. Why? Today, people buy online more than ever. This, in turn, breeds more and more opportunities for bad actors to take advantage of a lucrative market. Of course, companies try to protect their online businesses with e-commerce fraud detection software, but traditional tools can only identify the simplest scams; they cannot keep up with constantly evolving threats.
More and more companies are now resorting to more innovative e-commerce fraud detection methods. One of the most effective methods is powering up the process with artificial intelligence (AI).
In this article, we explain why modern AI solutions outperform legacy systems for fighting e-commerce fraud. But first, let’s take a quick look at the latest trends in the e-commerce fraud arena.
E-commerce fraud detection: Trends and statistics
The number of e-commerce frauds has significantly increased since the COVID-19 outbreak. Since the start of the pandemic-related shutdowns, 80% of medium-sized and 82% of large online retail businesses have reported a spike in fraud attempts. Businesses worldwide lost an estimated $20 billion due to e-commerce fraud attacks in 2021 compared to $17.5 billion in the year before.
Because of numbers like these, e-commerce companies have been forced to increase their investments in cybersecurity and fraud protection. According to estimates, the size of the e-commerce fraud detection and prevention market will reach $69 billion by 2025.
One of the most common scenarios in e-commerce fraud, called friendly fraud, is perpetrated by customers requesting groundless chargebacks for products they purchased and received. Friendly fraud accounts for up to 40% of the attacks experienced by online sellers. Next on the list, reported by over 30% of surveyed online merchants, are phishing and thieves testing whether stolen credit card numbers work.
As the incidence of fraud has increased, fraud scenarios have become more elusive and varied. Every new e-commerce innovation, such as buy now pay later (BNPL) or peer-to-peer (P2P) payments, breeds new fraud scenarios. The diversity and constant evolution of fraud scenarios make them hard to detect with traditional technology and calls for more effective AI-based e-commerce fraud detection solutions.
Telltale signs of e-commerce fraud
While there are many types of fraud, businesses should monitor for some common signs of potentially fraudulent activity. The most common red flags include:
- Higher than usual order volumes. An order way larger than what a customer typically purchases should spark concern. Especially if the order includes multiples of the same stock-keeping units (SKUs).
- Unusual IP address locations. If a customer who always orders from the US suddenly starts making purchases from Indonesia or Kenya, it should raise suspicion.
- Multiple orders from many credit cards. Many back-to-back orders paid by different credit cards from one buyer account indicate that criminals are testing stolen credit card details to see if they work.
- Repeatedly declined transactions. Such transactions signal that the person on the other side doesn’t have all of the required credit card information and is likely a scammer.
- Multiple orders and shipping addresses from one billing account. This can signify triangulation fraud when a scammer acts as a secret middleman in an online purchase.
Although these signs may seem simple to detect, in practice, manually catching every red flag would take forever, if it’s even possible at all. As for traditional e-commerce fraud detection systems, they are far from perfect. Usually, they can detect simple and well-known cases of fraud, but make numerous mistakes in the process. They also miss more sophisticated and atypical types of fraud.
This is where new, more accurate AI-based systems come into play. AI-based fraud detection can detect and prevent the most elusive fraud attempts with great accuracy.
How do AI-based systems do that? Let’s find out.
E-commerce fraud detection and prevention with AI
AI e-commerce fraud detection solutions are next-generation software that uses machine learning or deep learning algorithms to increase the precision of results with every assessed transaction. As such, they can identify not only existing weak spots in an online store but can also anticipate emerging threats so you can prevent them before they turn into financial losses. Below, we examine two methods of detecting fraud with machine learning.
In anomaly detection, an AI-based classification algorithm evaluates purchasing behaviors as either typical or fraudulent. Here’s how it works: an AI algorithm receives large volumes of data generated and stored by an e-commerce business. The algorithm looks at the properties of each transaction and classifies them as normal and abnormal. Normal in this case means behaving as is typical for valid transactions, whereas abnormal means atypical and, therefore, potentially risky behavior.
When detecting anomalies, an AI algorithm analyses what behaviors are typical for the majority of users, as well as for individual user accounts. So, if a certain behavior is statistically determined to be risky yet doesn’t deviate from what’s expected for a specific user’s account, the algorithm will account for this and not flag the transaction as fraudulent.
Anomaly detection is a quite straightforward method, generating only binary answers. Usually, it’s based on a supervised learning model, which means that the algorithm learns based on already classified (or labeled) data. In other words, AI scientists feed the algorithm with historical data that already have properties, aka “rights” and “wrongs” labels. The goal is to train the algorithm to tell good from bad in new, incoming data.
If the AI e-commerce fraud detection software identifies behavior as abnormal (aka risky), it classifies the datapoint as fraudulent, and your business’s specific policies determine what happens next. For example, it can block or suspend a transaction or notify your fraud team to investigate it.
Recognizing new fraud scenarios
On top of finding suspicious deviations from typical online behavior, AI systems can also identify emerging patterns of fraud even in scenarios that appear normal. The process of finding these threats is more complex and requires a type of AI called unsupervised machine learning.
The unsupervised learning model doesn’t use previously labeled data to learn about patterns; instead, it searches for and identifies patterns in historical data by itself. The idea here is that the system teaches itself to discover and then classify as yet unknown behavioral patterns and data correlations to identify fraud in new data. The system doesn’t simply classify the behavior as suspicious. It also distinguishes different types of fraudulent activity.
Combining supervised and unsupervised models produces the most accurate e-commerce fraud detection systems.
What types of e-commerce fraud can AI detect?
The variety of e-commerce fraud types is staggering, and it’s impossible to describe all of them here. Below, we list the most prevalent fraud scenarios and explain how AI can help prevent them.
In the case of identity theft, hackers obtain access to a user’s ID, bank accounts, or payment information and use it to buy things on an e-commerce website. It can often take the form of an account takeover, which is when a criminal hacks the user’s account for your online store and uses their payment details to make unauthorized purchases.
Consequently, the user whose account was hijacked disputes the unauthorized purchases and receives a chargeback, leaving your business to absorb the loss. What’s worse, however, is a potentially lost customer due to the tarnished trust.
How AI can help:
To detect and prevent identity theft, an AI system identifies odd activity in an account. The algorithm learns the previous behavior patterns of this user from historical data and picks up on an activity that doesn’t match their “fingerprint.”
If the user is new, the model uses data collected from an average user session on your website and compares it with the new user’s activity. If the activity differs from the “historical norm,” the AI algorithm flags it. With each new transaction, the algorithm’s classifications become more and more precise.
Merchant fraud occurs when bad actors create fake online stores, like one-day pop-up shops, and drive traffic to them with Facebook or Instagram ads. The scammers often create copies of legitimate stores and buy fake reviews to make them look trustworthy and attractive. Lured customers then buy products or services that likely don’t exist and thus, will never be delivered.
Triangulation fraud is another type of merchant fraud that’s been making lots of noise recently. This scheme begins much like the scenario described above: a fraudulent seller creates a fake store by copying an existing store on a marketplace. However, this time, when the fake store receives an order, the scammer uses the customer’s bank card to buy the ordered items from the real store on the marketplace and ship them to customers.
In both of these scenarios, the stores copied by the fraudsters experience substantial reputational damage, which in turn hurts their sales.
How AI can help:
E-commerce fraud detection works in this case by conducting behavior and sentiment analyses, the latter of which analyses customers’ emotions. These analyses detect suspicious activity and can then inform users and marketplace employees about the threats. For example, at Unicsoft, we created an anti-fraud browser extension that distinguishes scammers from legitimate online shops in a matter of seconds.
As the most common type of fraud attack, chargeback fraud (or friendly fraud) occurs when a customer requests a refund for a legitimate purchase under the pretext that they didn’t receive the product or that something was wrong with it.
The biggest issue with this type of fraud is that it’s hard to distinguish a fraudster from a customer whose shipment truly was missing or who’s legitimately dissatisfied with the product they received. It gives scammers the freedom to continue abusing company and bank policies. With this type of fraud, merchants suffer not only because of chargeback fees but also because of lost production, extra shipping costs, and banking penalties.
How AI can help:
To combat this problem and validate each transaction, AI detects subtle behavioral signs that all chargeback transactions have in common. From there, the algorithm learns to differentiate scammers from honest customers. If a user has a history of abusing the company’s return policies or their behavior is similar to that of scammers, the e-commerce fraud detection system will block the chargeback or suspend the transaction.
In the case of affiliate marketing fraud, the shop’s loyalty partners use affiliate links to spam traffic to the online shop and cash out on an unearned commission. The scammers abuse the affiliate system by creating multiple fake accounts or using clicking farms that imitate real traffic in bulk.
How AI can help:
It’s quite easy for AI algorithms to detect and prevent this scam by simply looking at how many accounts are tied to the same IP address. Once the algorithm detects a scammer, the system automatically blacklists them before they can abuse your policies.
The algorithm detects spam traffic in the same manner. E-commerce fraud detection software analyzes the typical behavior of website visitors and common traffic sources, flagging as risky everything that deviates from it.
Now, let’s now look at what makes AI-powered anti-fraud systems superior to static legacy fraud detection.
Benefits of using AI for e-commerce fraud detection
AI and deep learning analytics systems have proven superior to traditional e-commerce fraud detection systems. Here are the key benefits of implementing machine learning techniques in e-commerce optimization.
Real-time fraud detection
Traditional systems use retrospective analysis. As such, they can only detect anomalies that have already happened. AI algorithms don’t have this problem. They can analyze new activity and decide if it poses a threat. It protects you from vulnerabilities in your system and prevents potential attacks.
AI algorithms are constantly improving themselves by learning from incoming data. Every piece of information an AI system receives sharpens its e-commerce fraud detection precision. Every deviation from the norm further trains the algorithm, and it creates more complex rules to assess each transaction and reduce mistakes. As such, these systems surpass the capabilities of humans and traditional systems.
Fewer false positives
False positives are transactions conducted by honest customers but flagged as risky by anti-fraud systems. AI e-commerce fraud detection solutions tackle this problem. They analyze copious amounts of data about your users and their actions. This allows AI algorithms to spot exceptions and develop complex and specific rules to reduce false positives in the future.
Leveraging big data
AI algorithms can do something that traditional systems and an entire team of data analysts can’t — they can sift through huge volumes of complex information in a matter of seconds. Tapping into big data lets AI systems draw increasingly complex insights and make increasingly accurate predictions. And, of course, it automates the inefficient process of manual e-commerce fraud detection.
If we look at worldwide trends, it’s certain that e-commerce fraud will continue to increase and develop new and more elusive tactics. Although many e-commerce businesses still use legacy fraud detection systems, more and more players are opting for AI-fueled software. AI-enhanced software is more accurate, especially for detecting new, more sophisticated fraud scenarios and, thus, uncovering hidden threats. And unlike legacy systems, AI-based software only gets more precise and effective over time.
Do you want to power up your e-commerce fraud detection with advanced AI-based systems? Contact us, and let’s discuss the best way to implement AI into your business!