Rippleshot Blog

Why Banks Are Leveraging Machine Learning to Fight Card Fraud

Posted by Anna Kragie on 3, Nov, 17

Most financial institutions have recognized the true value of the technology effectiveness behind machine learning: The ability to discover patterns across millions of data points and hundreds of variables faster and more accurately than human beings. For predicting and stopping the use of compromised card details and the spread of card fraud, this technology is a game-changer.

The real obstacle forfinancial institutions boils down to the concept of implementing such a solution. Without in-house data scientists to create models, the IT resources to get the data from their internal systems or from their processors, and the expertise to insure that the data is clean and correct, implementing processes within an organization can seem overwhelming. But it doesn't have to be so complicated.

Solution providers who want to bring the value of machine learning to banks to help detect ATM breaches, thwart compromised cards, and reduce fraud need to be able to deliver a streamlined solution that takes days (not months) to implement, that does not burden a bank’s overloaded IT department, and delivers results in hours (not days or months). This a gap that exists in the marketplace that many companies are trying to solve, but have fully achieved.

Still, the need to detect, stop and combat credit card fraud is growing as financial institutions realize the rate at which fraudsters are refining their techniques to stay continually ahead of the curve. Banks are using new tools to fight fraud — machine learning, automation, cloud technology, etc. — but so are the fraudsters, making the monetization of compromised cards has become a sophisticated industry that financial institutions are working to keep up with.

This arms race to protect their cardholders, and their organization’s bottom line, has challenged banks to think smarter about how, why and where they should invest in better fraud compromise detection in order to respond to threats faster, more efficiently and more effectively.

How to Better Protect Cardholders from Card Fraud

Fraudsters are getting more sophisticated with their techniques, using bigger data sets and gaining access to far more than just social security numbers, emails, addresses and birthdates. As the fallout from the Equifax data breach has shown, the spread of stolen credit cards, synthetic fraud identities and the fraudulent mass creation of lines of credit, this problem is only going to get worse before it gets better.

A single approach to tackling card fraud is no longer enough to keep up with this rapidly-growing problem. A new report from U.S. News & World Report detailed this subject in a recent article, which includes insight from banking executives. For too long, financial institutions have relied on customer-centric approaches that insert too many manual review processes into the mix when attempting to track, detect and stop the spread of card fraud.

This is particular true when analyzing card data. Within each transaction there are droves of data and information that can lend itself to the studying of spending habit patterns. This rich data can be used to study the path of fraudsters’ behavior, predict the spread of fraud and halt massive data breaches from occurring.

“You’ve got to have a multi-layer approach,” Vince Liuzzi, chief banking officer at DNB First, told U.S. News & World Report. "It's critical that you balance the automated measures with exceptional customer service.”

Banks, however, can't overlook the value of faster, better card fraud detection as an element of customer service. The rise of data breaches has created an even great need for more software technology investments. 

That’s where the value of machine learning, data analytics and AI truly comes into play. Rather than waiting on manual review processes, or for alert systems to provide reports in a few weeks, banks should be demanding more in their card fraud compromise detection solutions. Automation is the future of fraud detection and banks should be embracing opportunities with more sophisticated technology in the market.

Where Opportunities Exist Within Machine Learning Technology  

Solutions need to be able to streamline the data feed process, automate the analytics process, offer continuous model refresh, and deliver actionable results within hours — and continue to do so on a daily basis. Successful machine learning solutions for financial institutions automates the tasks of gathering and checking data, detecting fraud, and validating the results. This frees managers to make strategic decisions, as opposed to getting mired in the mechanics of machine learning.

Successful machine learning solutions reduces the time from detection of fraud, or of compromised cards, to action to reduce as much fraud as possible from compromised cards/accounts, skimmed ATMs, etc. Deployed properly, machine learning that continuously sifts through millions of transactions and variables to deliver timely results can help financial institutions better and more cost-effectively address the growing card fraud problem.

Embracing Digital Transformation and Data-Driven Insights

As financial institutions continue down their digitization transformation — and invest in innovative technology — this opens the floodgates for more touch points for fraudsters to breach, particularly as it relates to card fraud. Card fraud and data breaches are rising at alarming rates, causing issuers to spend thousands each month reissuing cards, investing in new fraud prevention tools and combating new market threats.

With more sophisticated tools at their disposal, fraudsters are evolving as fast, if not faster than banks and payment networks. Thanks to machine learning, the digitization of data and artificial intelligence, financial institutions have access to the infrastructure and tools necessary to fight fraud — if they’re willing to invest money where it counts.