E-commerce Fraud Detection Using AI

E-commerce fraud has become a serious problem ever since the world has become increasingly dependent on digital media. A few decades ago, the online shopping industry was still in its nascent stage. However, times have changed considerably since then. As people become more inclined towards shopping from the comfort of their homes, we can see a spike in the number of e-commerce frauds as well.

Popular e-commerce service providers have to deal with thousands of orders every day. Out of these orders, many of them are card-not-present (CNP) purchases, which are much more difficult to verify than transactions where the card and cardholder are present. A study by LexisNexis Risk Solutions found that online fraud is up to seven times harder to prevent than fraud in person. Hence it is crucial for e-commerce companies to invest in a reliable fraud prevention system. Failing to do so can result in severe losses.

In this article, we will take a look into e-commerce fraud and how it can be prevented with e-commerce fraud detection solutions powered by artificial intelligence.

What is E-commerce fraud?

E-commerce fraud occurs when an unauthorized transaction occurs in an online store. Fraudsters generally use the compromised details of a stolen or fake credit card to carry out such transactions. This means that the merchant will be left without legal payment for the purchased product. Hence, the store will have to resort to charging the money back to the compromised customer.

E-commerce frauds can occur with orders that are Cash On Delivery (COD) and with prepaid orders as well. There are various types of e-commerce frauds that can be observed today. Some of the common types of e-commerce frauds are as follows:

•Return To Origin (RTO): Here, the customer asks for a return after the product has been delivered. They will use it temporarily and then swap it with a damaged product. They may also claim to have never received the product.

•Promo code abuse: A single customer will sign up multiple times on an app in order to get discounts through promo codes.

•Classic fraud: Fraudsters will steal or purchase a victim’s credit card details from the Dark Web. This method is usually adopted by newbie fraudsters.

•Triangulation fraud: This involves a fraudster, a legitimate shopper, and an E-commerce business. Fraudsters will set up an online shop at Amazon or eBay. This shop will sell high-demand products at unusually low prices. On acquiring the card details from the customers, the fraudster will purchase goods from a legitimate shop to send them to the customers.

•Interception fraud: Here, fraudsters will make an order where the billing and shipping address match the address on the card. After this, they will try to intercept the package before it reaches the address.

•Card validity testing fraud: Here, fraudsters will test different card details to reveal if the credentials are valid and then use them at another website to make unauthorized charges.

•Chargeback fraud: Here, customers will make an online order, but then ask for a chargeback because their card was stolen. This generally occurs after the product has been delivered. This type of fraud is very difficult to detect.

These are just some of the commonly witnessed online frauds. Fraudsters are perpetually looking for new and innovative ways to make money, and hence the types of frauds being committed will keep changing as time progresses.

Traditional fraud prevention

Traditionally, the common techniques used in fraud detection use static rules-based systems, often known as production or expert systems. There are several disadvantages associated with this, which make it less effective. They can be observed as follows:

•Usually, there will be a delay in identifying the need for a new rule and its implementation.

•Static rules-based systems depend greatly on human labour, which is expensive.

•Most of the rules that are being used for risk analysis are created by humans, who use their experience, knowledge and analytical skills. Unfortunately, fraudsters are becoming more creative with their techniques, which means that the rules have also become more complex and error-prone. This results in greater losses and false positives.

•Rules systems can increase to an alarmingly large size. This is because every identified fraud scheme is made into a new rule. As time progresses, the online retailer will acquire hundreds of different rules, which can be hard to analyze over time.

Artificial intelligence can be used to tackle these problems as it enables organizations to verify its performance and adjust to the changing reality more quickly. It can also help in devising a business strategy based on KPIs and generated predictions of fraud attempts.

Fraud detection with AI

From the above section, it is clear that traditional methods of fraud detection work with a rules-based approach that isn’t flexible and cannot adapt to new fraud patterns. There are some factors that may give an indication as to whether an order is fraudulent. For example, a user might have made an abnormally large amount of orders in the past few minutes, the user has entered a false address in the address fields or the user has skipped over the basic information needed for an order to be delivered, which will result in an RTO. It is impossible for humans to evaluate each factor and determine their contribution towards the fraudulence of that order manually. However, AI models can come up with complex rules in a very short amount of time and hence reduce cost, time and manual labour on this task. Generally, there are two primary types of machine learning algorithms that are used in such AI solutions — supervised and unsupervised. Both these types can be seen in fraud detection and prevention systems.

Machine learning algorithms analyze transactions and evaluate their threat score, usually between 0 and 1. This score is then ranked against a pre-established threshold that will mark the transaction as fraudulent or not. The main idea behind this is that fraudulent transactions have very different characteristics from legitimate ones. ML algorithms can recognize these patterns in the data, enabling them to detect orders from fraudsters.

AI solutions analyse hundreds of data points from millions of transactions to identify patterns that might constitute fraud. They often find patterns that human beings would miss. The moment an AI algorithm detects an abnormal order, it will either automatically block it or refer it to a human expert for review.

Take the case of Almundo.com. This website is one of the top online travel websites from Latin America. They use AI solutions to reduce fraud, chargebacks and manual reviews by 70%. Such a reduction leads to better customer experience (less false positives), optimization of operational costs and a significant increase in revenue. In the e-commerce industry, online retailers have to handle voluminous datasets. This is why machine learning algorithms and models are crucial for them to function effectively. These algorithms can evaluate large numbers of transactions instantly and are always assessing and processing new datasets. Since the industry completely depends on internet connectivity and banking for online purchasing, it is extremely susceptible to fraud or deception. The machine learning model gets more accurate and efficient with large datasets as it can distinguish and simplify multiple behaviors. When it comes to detecting and preventing e-commerce fraud, the machine learning model helps in this process by gathering and categorizing data, then fed with training sets to envisage the possibility of fraud.

Benefits of using AI in fraud detection

There are some major benefits associated with using AI in detecting fraudulent transactions in e-commerce. They include-

•Real-time processing of data

Traditionally, fraud detection systems function in accordance with circumstances that have already occurred. This means that they can only prevent the types of fraud that have already happened. However, AI algorithms can consider changes in real time and act on a fraudulent attempt even before the attack.

•Finding hidden patterns

An AI solution is excellent at finding hidden correlations beyond human capabilities. Additionally, it becomes better at finding new scenarios and preventing them with every discovered threat.

•Proxy and VPN detection

Legitimate shoppers don’t usually use VPNs while purchasing products. ML models can detect proxies and VPNs, which can come in handy as most fraudulent transactions have this one commonality.

•Consistent results

Human mistakes are completely normal and occur quite often. However, ML algorithms don’t make any mistakes at all and guarantee complete accuracy. By using such a system, organizations will get consistent security without occasional breakdowns because of human error.

•Behavior analytics

ML algorithms register the usual behaviour of each customer. So, it is easy for them to notice any deviations and spot suspicious behavior.

•Quick and accurate verifications

Automated verification can speed up the whole purchase process for the client and operate on defined rules, eliminating the mistakes human employees might make.

It is quite evident that if online retailers want to prevent major losses that occur through fraud, they need to consider investing in an e-commerce fraud detection system that is powered by AI. This can not only improve the accuracy of recognizing fraudulent transactions, but can also help prevent them from occurring in the future.

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