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.
What is holding the financial institutions back?
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.” […]