Powering Fraud Prevention With Machine Learning

03-May-2018

Artificial intelligence (AI) and machine learning are nothing new. In fact, machine learning has been used for more than a decade to enhance technology and improve functions in everything from smart toys to the Roomba to technology that identifies and mitigates fraudulent activity.

Jump forward 10 years, and fraud shows no signs of slowing down. Businesses, including those that support the payment processing ecosystem, are working around the clock to protect their assets (and their customers) from criminals who are continually looking to exploit weaknesses for the greatest gains. Study after study confirms that fraud is a major concern today for all parties invested in the global payment processing community.

Anticipate and prepare

Companies are at a disadvantage. It's increasingly difficult for the good guys to keep up with the pace of fraudsters, who operate quickly with no rules and few barriers. Companies must balance this increasing risk while continuing to grow their businesses – adopting new payment options, receiving transactions across new devices and ensuring their customers' user experience stays aligned with growing expectations.

Criminal attacks come from multiple fronts, including leveraging weaknesses in Internet of things-enabled devices, creating wide-reaching cybercrime rings that carry out well planned and executed fraudulent activity, and preying on victims through government processes. Regardless of their market, businesses need to ensure their fraud prevention systems are on the top of their game – not only prepared for today's fraud tactics, but also able to anticipate and react to the evolution of fraud for tomorrow.

Take a multilayered approach

What should online businesses look for when evaluating third-party offerings for protection? A vendor that offers a multilayered approach to deter fraud. At the base of any enterprise fraud solution should be a combination of unsupervised and supervised machine learning. Unsupervised machine learning capabilities can assimilate billions of data points to determine transaction validity, scoring, device identification, geolocation, IP proxies, dynamic monitoring, continuous authentication and more – while supervised machine learning can simultaneously analyze that data to create a rating that shows the relative risk and safety of a transaction.

The combination of both types of analysis is important in combatting fraud. It's significantly more beneficial to have access to the current environment and historical insights, as well as relative risk and safety of the transaction, which allows organizations to make smarter decisions. Applying advanced analytics to fraud detection is essential to ensure confidence with every transaction, while addressing the ever-changing tactics of fraudsters and allowing customers to approve more orders, uncover new revenue opportunities and improve profits.

Harness technology

With as many as 1.2 million fraudulent transactions happening every 200 milliseconds, the right AI and machine learning technology is critical for not only combatting today's fraud but also stopping future attempts.

Access to data is easier and the cost of data is cheaper than ever for online criminals. It's important that companies have layered levels of protection and technology, along with their own sources of data, in order to analyze hundreds of data points about an individual transaction or digital interaction. Look for a solution that's dynamic and constantly reviewing new sources of data. This will allow your technology to make changes over time and identify areas of improvement.

Whether it's the government, an airline or mobile apps, one thing is clear: no one is safe from fraud. And as fraud evolves, businesses need to make sure they are leveraging the latest technology, including machine learning, to keep themselves and their customers safe from cyberattacks.