Increasingly, as fraudsters get smarter and faster, you'll find the mention of AI in discussions about tools to combat the rise of card fraud. Security professionals have already bought in and are leading the charge for more advanced technology investments.

In fact, a recent Forbes article cited a stat saying "80% of fraud specialists using AI-based platforms believe the technology helps reduce payments fraud." To that same effect, this same percentage of fraud specialist have noted they've seen "AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts."

What's leading this push toward AI is the application of predictive technology that can help organizations, such as banks and credit union leaders and credit unions, learn how to prevent fraud before it actually happens by proactively spotting patterns. Of course, Rippleshot has bought into this philosophy, as are a growing number of financial institutions.

That same Forbes piece noted that "63.6% of financial institutions that use AI believe it is capable of preventing fraud before it happens, making it the most commonly cited tool for this purpose." This data was sourced from a PYMNTS.com report, AI Innovation The AI Innovation Playbook that takes into account insight from 200 financial executives from commercial banks and credit union leaders, community banks and credit union leaders and credit unions across the United States.

What's driving this latest attention?

Payment Fraud is Getting More Complex: Financial institutions need the sophistication of technology that combines big data, AI, machine learning, rules-based logic and predictive models. Those tools alone aren't useful, but combined can be exceptionally effective at spotting, stoping and preventing the spread of fraud.

AI Technology Is Faster Than Humanly Possible: Today's payment fraud needs solutions that can act faster and at the scale at which fraud spreads itself. Addressing fraud risk and evolving fraud methods needs to be faster than manual analysis and humans can can compute today.

AI Acts In a Matter of Seconds to Spot Patterns: There is rich insights that exist within data patterns. The problems many financial institutions face is that they don't know what to do with all their data, don't have the access to enough data or don't have a dedicated fraud team to manage this complex process. AI and predictive technology tools find anomalies at scale in a way that manual processes can't compete with.

Forbes Contributor Louis Columbus gave his insight into what's driving these trends: "AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows."

PYMNTS research unveiled some key findings:

• "FIs see AI’s potential to more effectively fight fraud, but most don’t use it": Of those who do use it, they use it to fight fraud often; 45.5 percent use it as part of their fraud prevention efforts.
• "FIs believe AI’s real benefit is that it reduces manual review and exception processes": 66.2 percent of FIs’ fraud specialists see reducing the need for manual review.
• "FIs have concerns about AI’s complexity and transparency": One apprehension to adoption is that many believe AI is too complicated and time-consuming compared. This is changing as AI becomes more common at the major FIs.
• "FIs express strong interest in using AI’s dynamic capabilities to improve fraud prevention." 66.7 percent of FI respondents believe the solution would help reduce manual review.

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Where AI and Machine Learning Fit Into the Evolution of Card Fraud

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Fraud and fraud patterns are evolving and change more rapidly than financial institutions can keep pace with. This is where the value of machine learning and data analytics comes into the mix.

The very nature of Machine Learning is to learn from the data it is processing, adapting to changing trends or relationships in the data. Detecting and mitigating fraud to manage risk involves in-depth data analysis to identify relationships and trends to pinpoint where and when the fraud originated.

Relationships and trends are becoming leading indicators of outcomes (like fraud). As these leading indicators emerge in new data, outcomes can be predicted and acted upon. A data analytics approach equips issuers with the tools to understand what’s happening across their own card portfolio — and how to detect risk. But you have to have access to that data and be able to make sense of it all.

Rather than waiting on manual review processes, or for alert systems to provide reports in a few weeks, banks and credit union leaders should be demanding more in their card fraud compromise detection solutions. AI is driving is the future of fraud detection and banks and credit union leaders should be embracing opportunities with more sophisticated technology in the market.

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 AI and 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 AI and machine learning.

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. 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.