The cost of fraud is rising. According to CNBC.com, fraud and identify theft cost consumers more than $16 billion in 2016 – nearly $1 billion more than in 2015.
LexisNexis research cited on chargebacks911.com finds that for each dollar lost to fraud, “online merchants can ultimately expect to lose $2.40 in revenue due to the associated fees, lost merchandise, sales potential, and more.”
Machine learning promises to become our best weapon in the war on fraud. But, why, all of a sudden, is it the panacea to the world’s fraud epidemic, and just how will machine learning affect your own security strategy?
The Ultimate Supercomputer
New advances in technology and science have dramatically enhanced the performance of machine learning algorithms, making them much more capable than they were just a few years ago.
Today’s machine learning systems also vastly outperform the modern neural network, pulling in and distilling far greater amounts of data by comparison.
Plus, they streamline and automate fraud detection in unprecedented ways. This is because machine learning systems evolve and improve their performance over time, without explicit programming.
Not only do they “learn as they go,” but they also learn at a mind-boggling pace. Machine learning platforms today can identify even the most obscure threats in real time, catching and blocking new instances of fraud as they occur.
Addressing Big Data Challenges
According to the Nilson Report, credit card transactions rose 48 percent, debit card transactions 46 percent, and electronic transactions 45 percent between 2010 and 2015, for a collective increase of 34.2 billion transactions annually. The proliferation of data can weigh heavily on traditional fraud detection resources.
Transaction data typically spans disparate systems and applications as well, which further complicates fraud detection – especially with new mobile wallet, IoT, P2P and digital banking technologies hitting the market daily. As LexisNexis reports, “fraud through remote channels is up to 7 times as difficult to prevent as in-person fraud.”
Fighting Fire with Fire
But the most compelling reason to embrace machine learning now is this: Fraudsters are constantly evolving their tactics, and they are starting to use the technology themselves.
This means that when your credit union creates a new rule going forward, tech-savvy fraudsters will find it much easier to get around it. Machine learning can deliver the speed and flexibility needed to stay ahead of their advancements.
To protect credit unions and their members in this new era of fraud, our team at CO-OP is developing a machine learning platform that unifies transaction data across all our systems and applications.
Initially, the platform will work side by side with advanced neural network technology. Over time, we may switch to machine learning entirely or keep both systems in place as the ultimate safeguard. In the near term, we expect to have the platform in place on the account side by the end of this year, with credit and debit systems to follow.
The Importance of Scale
Achieving scale is critical to the success of any machine learning implementation; the more data these systems can access, the better they perform.
This year, CO-OP is on track to process more than 4 billion transactions. While we won’t share real data across credit unions, we will aggregate it for modeling. This means that whether you’re a $3 billion credit union or a $300 million credit union, you’ll receive all the benefits of our new machine learning technology.
AI and Digital Transformation
Fraud remains a hot topic. Because the cost of fraud is so high, our investment in machine learning will reap dividends for our organization and client credit union community for years to come.
The initial process of aggregating data alone brings with it far-reaching benefits. An important step in our own digital transformation journey, data integration at CO-OP will enable, for example, advanced predictive analytics and other forms of AI.
Ultimately, machine learning is probably the most important technology to emerge in the past five years. The fact that credit unions will soon be able to put it to work full force – first against fraud, then to improve marketing and the member experience – is big news for the industry.
To learn more about advances in artificial intelligence and machine learning technologies – including CO-OP’s new solution set to go live in 2017 – download the white paper, “A New Frontier: Machine Learning, Artificial Intelligence and Big Data” here.