Digital Banking

Using Both Human And Artificial Intelligence To Keep Credit Unions Safe From Cybercriminals

The motto for banks turning to artificial intelligence (AI) and machine learning to foil fraud is “Work smarter, not harder.” In the latest PYMNTS Digital Banking Tracker, a Feedzai collaboration, Shazia Manus, chief product and strategy officer of CO-OP Financial Services, explains why smart is hot and how even small FIs can get some AI action. Also, find the latest headlines on how digital savvy banks are emphasizing personal touches — from human-esque conversational AI to a family-focused remittance app — and more. Plus, a scorecard of more than 200 industry players, all inside the Tracker.

Fending off fraudsters is no easy task for banks and credit unions (CUs). That’s especially true for smaller financial institutions (FIs) that may not have the resources for a cybersecurity redo.

But, can the old adage of working “smarter, not harder” also help those fighting against financial fraud?

That’s the goal behind a new wave of cybersecurity technology being deployed and adopted by FIs in the United States and around the world. Increasingly, banks, credit unions and financial services firms are turning to new technology like artificial intelligence (AI) and machine learning to help safeguard their customers from breaches like those that have garnered headlines in recent months.

According to recently published research, the financial services field has become one of the biggest adopters of AI and machine learning technologies, ranking alongside telecommunications and other high-tech fields. Considering the benefits the tech can have on fields like security and customer relations, it’s no wonder it’s popular. In fact, more than 25 percent of FIs and service firms are offering AI applications and another 10 percent-plus plan to implement it in the coming years.

Even with relatively high adoption, though, 75 percent of FIs or financial service firms have not yet implemented AI and machine learning. So, what’s holding these FIs back? In a word, cost.

That’s especially true for smaller FIs like credit unions which may not have the funds for a full-scale, AI-inspired overhaul. In a recent interview with PYMNTS, Shazia Manus, chief product and strategy officer for North America-based FinTech company CO-OP Financial Services, explained that such advanced technology can be well within credit unions’ reach — including many of the institutions with which his company works — as long as it is used intelligently by the humans at the helm.

“We never want to use any new technology just for the sake of having the latest and greatest,” Manus explained. “If this is going to be accessible and effective, it needs to be contextualized to solve a specific problem or issue for a business — and, that’s what we try to do with AI and machine learning.”

Adding AI and other new technologies

CO-OP Financial Services is developing AI and machine learning to be used on behalf of its CU clients, enabling the company to provide alert and case management capabilities for fraud and risk services.

Manus explained that, because layering in new tech can often be a risky endeavor, the company worked to build a system that would allow them to adopt new technologies efficiently.

“We’re fortunate to have a long history of success in our industry, but sometimes when you have that, there’s a tendency to just do things the way you’ve always done them,” she explained. “So, one of the challenges for us has really been just stepping back and developing a process for implementing these new technologies.”

One of the important elements of that process was using some human intelligence when adopting AI, Manus said. Rather than looking for any opportunity to add the capabilities to its offering, the credit union service organization wanted to find specific use cases in which it was sure the technology would have a tangible impact on CUs and their members.

As a result, CO-OP Financial Services studied where the use of AI and machine learning could make the biggest difference before implementing either. The company has worked to develop use cases and processes for those capabilities, including fraud prevention and customer authentication. Earlier this year, Manus noted, CO-OP announced it would invest $20 million in technology innovations, including developing AI- and machine learning-assisted fraud capabilities as part of a partnership with data science company Feedzai.

“A lot of our efforts with this technology have been designed to leverage the transactional data that we already collect,” she explained. “We’re working to collect that data and using machine learning and artificial intelligence tools to identify and predict any problems or risks.”

The company also intends to use the technology for customer onboarding and risk evaluation, among other use cases.

Keeping AI accessible

While CO-OP Financial Services is adopting AI applications, Manus acknowledged the difficulty for some firms to make the investment in a new and still-evolving technology.

In the short term, she recommended that FIs and financial service firms of any kind look for ways to implement AI and machine learning to improve the experience for customers, including use cases surrounding fraud prevention and using the technology to predict and prevent credit and debit card fraud.

“As a credit union cooperative, we have the size and the endpoints, as well as tremendous leverage with different partnerships that make it easier for us and our members to use this technology,” Manus said. “For smaller institutions, it’s about finding the right problems we know are worth solving for, and identifying those use cases to build solutions for first.”

In the long run, she explained, more FIs and service firms will likely be able to add AI and machine learning capabilities of their own as the technology ages. Over time, it will naturally become more cost-effective for more companies.

“The cost of this technology is really going to continue to go down and it’s going to continue to become more accessible,” Manus said. “Now it’s more about really knowing the use cases, and how you are going to use AI and machine learning to solve concrete problems for customers and transform the way you do things.”

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About the Tracker

The PYMNTS Digital Banking Tracker™ brings readers the latest news, research and expert commentary from the FinTech and consumer banking space, along with the rankings of over 200 companies serving or powering the digital banking sector.



The pressure on banks to modernize their payments capabilities to support initiatives such as ISO 20022 and instant/real time payments has been exacerbated by the emergence of COVID-19 and the compelling need to quickly scale operations due to the rapid growth of contactless payments, and subsequent increase in digitization. Given this new normal, the need for agility and optimization across the payments processing value chain is imperative.