How Machine Learning Helps With Fraud Detection

Fraud detection with machine learning requires large datasets to train a model, weighted variables, and human review only as a last defense.

With advances in computer technology and ecommerce also comes increased vulnerability to fraud. Hackers are continuously finding new ways to target undeserving victims, from stolen credit card details to false accounts. Any business or individual who uses online payment sources is open to fraud.

In 2015, financial fraud — including payment cards, remote banking and cheques — rose a staggering 26 percent from the previous year, totaling a cost of £755 million. It was the fourth consecutive year that has seen an increase in this area.  It is the most common crime in the UK, with 2.47 million offenses reported in 2015-2016 alone. The multi-million pound cost of fraud  is proving detrimental to online businesses who foot a high proportion of the bill in chargebacks.

In 2015 all fraud types had risen with much owed to the growth of impersonation and deception scams, as well as sophisticated online attacks such as malware and data breaches.

The traditional approach to tackling this problem is to use rules or logic statements to query transactions and to direct suspicious transactions through to human review. While there is some variation, it is notable that over 90 percent of online fraud detection platforms still use this method, including platforms used by banks and payment gateways. While this is effective to some degree, in cases where there is a sufficient gap between an order being received and goods being shipped, it is also incredibly costly and far slower than alternatives.

The “rules” in these platform use a combination of data, horizon-scanning and gut-feel. The system is backed with manual reviews to confirm experts’ decisions. If we take the recent reports of an abundance of Turkish credit cards available on the darknetdue to the publicized data breach in Turkey: businesses recognize the increased risk of Turkish cards as fraudulent and can simply add a rule to review any transactions from Turkish credit cards

Following this, every attempted purchase made by such a card raises an alert and is declined or reviewed. However, this raises two significant issues. The first is that such a generalized rule may turn away millions of legitimate customers, ultimately losing the business money and jeopardizing customer relations. Secondly, while this can deter future threats after such fraud has been found, it fails to identify or predict potential threats that businesses are not aware of.

These rules tend to produce binary results, deeming transactions as either good or bad and failing to consider anything in between. And until the rules are manually reviewed, the system will continue to prevent such transactions as those from Turkish credit cards, even if the risk or threat is no longer prominent.

Criminal gangs also use malware and phishingemails as a means to compromise customers’ security and personal details. Once obtained, fraudsters will use these details to access customer accounts or to commit fraud. These methods all aim to compromise customers’ personal and financial details, including card data, in order to enable the criminals to commit fraud. Here, the card data used is legitimate but is not being used under the consent of the owner. In these cases, such rules would fail to block transactions.

According to the 2015 Merchant Risk Council (MRC) Global Fraud Survey, merchants typically manually review 10-15 percent of online orders. Other reports suggest that as many as 26 percent of ecommerce orders are manually reviewed.