Rippleshot Blog

Machine Learning 101: The Future of Fraud Protection for Banks and Credit Unions

Posted by Anna Lothson on 7, Jul, 17

FinTech advancements have transformed the banking industry in the past decade faster than ever before. Paving that path has been the ability of banks and credit unions to tackle one of their biggest problems (fraud) through one key trend: Machine learning.

What's made that all possible? Software and options to integrate smarter, better fraud detection tools. 

While most of the chatter around how artificial intelligence (AI) will impact how banks and credit unions interact with their customers (i.e. chatbots) in the near future, there’s a lot more powerful ways organizations can leverage machine learning to actually impact their bottom line.

A survey from Accenture with data from 600 bankers provides some insight into this topic, suggesting that AI will become the main way banks will interact with their customers in the next few years.

"The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we've seen coming through here is the view that technology will actually help banking become a lot more personalized," said Alan McIntyre, head of the Accenture's banking practice and co-author of the report.

But when it comes to implementing machine learning into other business practices (fraud detection, etc.), many industry surveys still report there being skepticism about integrating new technology.

According to one study, bank leaders indicated “siloed data sets, regulatory compliance, fear of failure, and unclear internal ownership of emerging technologies” have kept them from embracing technology innovations.

The problem with that mindset, however, is that fraudsters are getting smarter — as is their techniques and technologies. With fraud rapidly on the rise, banks need to think strategically about how they both detect, prevent and combat fraud. Not to mention manage the rising costs associated with reissuing cards once an account is breached.   

Interestingly enough, Rippleshot’s team discovered that over 50 percent of fraud that’s not being caught was correlated to data breaches. While many banks have fraud teams trying to tackle this problem alone, taking a manual approach to fraud detection isn’t sophisticated enough for today’s data breach-filled ecosystem. With so many innovations in the marketplace, banks shouldn't have to manage the cost of fraud alone.

Enter the power of machine learning and third-party software providers.

MACHINE LEARNING: TAKING A MORE STRATEGIC APPROACH TO FIGHTING FRAUD

When thinking about the most valuable data sets a bank is equipped with, what comes to mind is customer account information. Unfortunately, cybercriminals and hackers are collecting that data on a daily basis.

That’s why, regardless of if banks are ready for the future, AI and machine learning technology already has the power to transform the financial services industry in ways not possible before. Luckily, this technology allows banks to thwart off breach threats faster, detect breaches when they occur, and devise a plan of attack for when breaches hit — preventing them from spreading into even bigger problems.

“Machine learning uses technologies that can self-learn with little to no human intervention. They get better at what they need to do, the more information you feed them. The whole idea for machine learning is that those technologies don’t require constant human intervention to get better and better,” Sridhar Rajan, robotics and cognitive automation lead for Financial Services at Deloitte Consultants, told Banking Exchange in an interview. “They eventually mimic human judgment at high speed, high scale, and low cost.”

MACHINE LEARNING AS A TOOL FOR ENHANCED FRAUD MONITORING AND MITIGATION STRATEGIES

By infusing machine learning technology into the fraud detection process, bank and credit union fraud teams can be better equipped to get ahead of the problem before it spreads. Eliminating costly, manual processes that are also far less accurate (not to mention far slower) presents endless benefits for financial organizations.

Having the ability to sift through organized data that’s collected using powerful software, instead of relying on a team of data scientists to interpret what accounts have been breached (and which will actually go fraudulent), is the only way to devise sustainable business practices and be fully prepared to fight fraud.

Did you know? Only 1-5 percent of compromised cards ever actually go fraudulent. What that means is that many banks and credit unions are spending thousands a month unnecessarily reissuing cards that likely weren’t going to go fraudulent.

For example, Sonar, Rippleshot’s smart fraud detection and card reissuance solution, automates card compromise detection and the reissuance process for issuers. Leveraging the power of machine learning, Sonar processes millions of card transactions daily to proactively detect compromised merchants and identify an issuer’s at-risk cards. Sonar’s Fraud Forecast™score assesses each card’s total exposure from all breached merchants to determine the probability that it will become fraudulent.

For comprehensive details on how machine learning technology can significantly strengthen financial institutions' fraud monitoring efforts, review how Rippleshot Sonar automates card compromise detection and the reissuance process for issuers.