Credit unions must continually evolve to protect against the growing sophistication of financial crime. Traditionally, rule-based solutions have been used to fight financial crime threats. But using rule-based models alone are no longer sufficient. That’s because there are three main problems with rules:
Credit unions should make the most of their resources and become more proactive in improving efficiency. Many are turning to AI and machine learning to solve issues.
In the past, the idea of a machine autonomously learning and adapting was quite intimidating. To better understand how AI can help, it’s best to look at the two main areas where it most drastically improves efficiency: operations and threat detection.
AI (and machine learning by extension) enable autonomous analytics at scale for every member account to ensure that alerts are only generated for true, risky behavior.
Machine learning enables monitoring and threat detection with as few resources as possible, which is crucial for managing day-to-day threats. Understand the types of machine learning, the differences between them, and the effort required to ensure these models work effectively:
Fincrime will continue to increase, but the same can’t necessarily be said for the size of a credit union’s staff or budget. Make sure tools and technology empower you to do more with less. Credit unions are quickly adopting AI and machine learning to work smarter—not harder—and stay one step ahead.