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Machine Learning: How It Can Prevent E-Commerce Credit Card Fraud

Updated: Dec 12, 2019

The use of credit cards is common in e-commerce. Many online businesses allow the use of these cards for various transactions. When doing online shopping, you are often asked to provide the account details of your credit card to complete the transaction. However, when the site is not secured, and you are not careful with your online pursuit, you are putting your financial information at risk.


Online credit card fraud has increasingly become a problem for many online businesses nowadays. Many credit card account holders complain about other people using their information, and they blame some unsecured websites that have the potential to leak card information.


Thanks to modern technology, the rise of machine learning has come to the rescue. The function of artificial intelligence exists to assist human transactions and address common problems in the digital world. Machine learning can be an efficient method in creating predictive models to deal with vast amounts of data and information.


Take, for instance, the use of payment gateways, such as eWay, PayPal, and Stripe. They use machine learning techniques that have the potential to reduce credit card fraud.


Vast Amount of Data and Information


Many companies have access to billions of credit card information. These companies can use machine learning to identify indicators for fraud. The machine can have algorithms that can automatically detect future fraudulent transactions. It can be self-learning, artificially intelligent system that allows businesses to protect data and information.


Behavioural Patterns


Machine learning can also address the existence of what we call false positives. False positives occur when a transaction is flagged as fraudulent, but it’s the actual customer who does the transaction. Here, various aspects of transactions are considered, such as the sale amount, duration, number of cards used, location, and many more. Through these, machine learning can analyze behavioural patterns and predict if a particular transaction is fraudulent or not. Once determined, it can act on its own to either block the transaction or ask for additional security information.


Auto-Flagging


Typical fraud detection software identifies fraud by analyzing flagged transactions for a business. For instance, if a single customer tries to use multiple cards for one transaction, this may be tagged as potential fraud. If the software has access to historical and current data, it can learn to spot fraud classifiers all by itself. This can end up saving a business a lot of wasted effort and time. With fraud prevention software, your e-commerce platform becomes more secure and safe.


Proxy detection


Finally, machine learning can also detect proxies. It’s common how online tricksters use proxies to hide their real location when doing fraud transactions. Machine learning can compare the time it takes to connect with the user’s IP address with the time it takes to connect with their browser. A large discrepancy can be considered a red flag.


Machine learning can help address the problem of credit card fraud that is common in e-commerce. Machine learning can deal with large amounts of data and information, digitally analyze behavioural patterns, automatically flag any fraudulent activity, and go as far as detecting proxies employed by online crooks. As a business owner, it is worth it to explore how you can integrate machine learning into your operations.


If you’re looking for an e-commerce accountant in Australia, get in touch with us today for a free consultation!

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