Fraud exists only where there is money. Or any other physical or intangible objects that have value. In the 21st century, fraud exists where there are money and data at the same time, that is, in the field of electronic commerce (and not only here).
This is like running in a vicious circle – while companies are looking for ways to defend themselves against old threats, scammers come up with new ways to fool everyone around. However, there is a chance that fraud detection through Machine Learning can protect both merchants and customers.
Everything you need to know about e-commerce fraud and the ways to prevent it is in this article.
What Is E-Commerce Fraud
Fraud in the field of electronic commerce is defined as illegal intentional actions aimed at committing an unfair transaction when purchasing goods or services in electronic space. At the same time, both individuals and companies can be the subject of a crime.
Moreover, this is one of the most attractive areas for fraudulent transactions because to this day it is quite difficult to track fraudulent attempts and the people who commit them. The following statistics confirm this.
E-Commerce Fraud Statistics
For now, 92% of fraudulent attempts involve credit cart but it is predicted that card-not-present scam is predicted to grow by 14% by 2023 (and turn out into $130 million losses for retailers);
The greatest amount of eCommerce fraud attempts takes place in the USA, and users from 25 to 34 years old are the most frequent victims.
There is the scientific observation that e-commerce fraud is rising in response to e-commerce sales growth. Obviously, this is just the vicious circle we have mentioned at the beginning.
E-commerce fraud can be classified into several types. The following infographic shows four of them, however, this classification can be expanded.
Credit Card Fraud
Credit card fraud is the most common type of financial fraud per se. In the case of e-commerce, the goal of the attacker is to steal the card data and make the maximum possible number of purchases in different stores until the card limit is reached.
In most cases, the user understands that the money from his card was spent in stores after everything was completed, and the stores were not affected at all. However, anti-fraud protection for an online store can be a good enhancer of reputation and reliability.
Return to Origin
In this case, the attacker can even use his personal credit card, or cash to complete the transaction. The fraudulent scheme is to buy goods and return the fake in a few days according to the conditions of return. This type of fraud is common for products that are expensive but that are easy to fake visually. A classic example is fake iPhones.
Promo Codes Abuse
In the case of fraud of this type, the attacker uses several promotional codes for one product to get the maximum discount. This is difficult to implement technically – and also difficult to track.
According to the study, users from the Millennials generation are the most beloved victims of attackers who work according to this scheme. The thing is that millennials easily share their personal data on social networks, and fraudsters can easily draw a portrait from a person’s data – that is, correctly record his name, date of birth, city of residence, occupation and even the name of a pet (the latter is often used as an answer to a secret question, for example, to change the password on the card).
Fishing is the oldest financial fraud model. The bottom line is to create a site almost identical to the popular eCommerce platform, lure users through a fake e-mail newsletter and force them to enter their financial data and passwords. After that, this information is used for further fraudulent activities – for example, credit card fraud.
As soon as the number of purchases made with mobile devices began to grow, scammers immediately drew attention to this trend. The following infographics prove this clearly.
account takeover when a fraudster steals access to the user’s account
data breaches which mean data theft with the help of mobile devices
call center fraud when a fraudster pretends to be a call center employee and forces the customer to enter his data on a fishing website, for example
subscription fraud is a case when a fraudster subscribes to services using the victim’s name and phone number.
The last one is very difficult to track or prevent, however, employed machine learning can help with it. Here is how.
Why Machine Learning Is Efficient to Minimize Ecommerce Fraud
At the moment, machine learning and artificial intelligence are the most promising technologies in the context of combating financial fraud. And that’s why.
It Is Able to Process Data in Real-Time
Outdated fraud recognition systems could work only within the framework of established scenarios – and could make an accurate conclusion only after the attempt was successful. Machine learning algorithms track changes in data in real-time, that is, they can detect a fraudulent attempt at the preparation stage. According to research made by Proxyway, even the biggest marketplaces like Aliexpress, Amazon or eBay must further strengthen their screening operations and react a lot faster.
Here is a good video that explains the importance of real-time fraud detection in the real-time world and describes how does it work.
It Catches Unnoticeable Anomalies
The machine learning system is not limited to a clear set of scenarios – it constantly learns through worked out situations. This means that with each scenario passed, the probability that the system does not track the slightest deviation is extremely small.
Facial Recognition Feature Can Prevent Fraud in Physical Stores
This is a relatively new technology at the crossroads of machine learning and artificial intelligence, but it is promising to prevent fraud in real stores. For example, such cameras can help in case of identity theft or credit card fraud when a fraudster makes order online but comes to pick up the goods in the store.
Proxy server detection is the first signal of attempted fraud. As a rule, users who make honest purchases do not try to hide anything.
The behavior analysis function makes it easy to distinguish fraudulent attempts from legitimate ones due to the fact that the system collects data about typical client behavior and catches anomalies that can be interpreted as theft attempts.
If we talk about the practical application of ML in finances, then here are the numbers proving expediency. The fraud detection and prevention market (FDP) is expected to exceed $63 billion in 2023. It is also predicted that 77% of companies are going to invest in big data solutions in 2021, and 72% – in ML technologies with the aim of fraud prevention as well.
How to Protect E-Commerce Store With Machine Learning
Fraud protection is a real headache for online store owners. Here are a few highlights to focus on.
Protect user’s accounts
The least you can do is motivate users to come up with complex passwords. A machine learning system can help track their behavior and signal a possible violation, for example, if the user makes an atypically large purchase.
Focus on mobile fraud prevention
As we have said, mobile fraud is growing and taking on new forms. If you know that your users interact with you via mobile, their (and your) safety should be a priority.
Employ proxy detection if you are delivering goods to other countries
If you send goods to different countries, then you definitely need to have a system capable of proxy detection. The fact is that scammers can use this method to create the illusion of orders from different countries and from different users.
At the moment, the machine learning system is the most progressive way to protect against financial fraud for all areas without exception, and for electronic commerce as well. Use this opportunity in 2020 to protect your money, goods, and reputation.