When discussions about fraud, payments and security arise, you won't get long into a conversation before machine learning and artificial intelligence (AI) come into the mix.
Through the application of high-performance software, machine learning technology has created advanced computing abilities that have a broad-scale reach for community banks that allow them to better compete against the bigger banks. This has created a new reality for issuers looking to enhance their fraud detection tools beyond basic what's readily available in the marketplace today.
From a financial services perspective, machine learning technology is useful because of its ability to automatically processes data to create predictive fraud models — enabling issuers to strategically manage their fraud loss and reissue management strategies. But Machine learning isn’t just about fraud detection — it helps issuers gain access to, and have a better understanding of big data, and how to apply it to real-world scenarios on a daily basis.
As more investments across the public and private sectors are put into technologies like machine learning that allow data scientists (like those at Rippleshot) to fully make sense of millions of data points in order to help organizations better understand the patterns that exist within their own data — and what they can do with that data — the better off the entire payments ecosystem will be.
Of course, AI and machine learning aren’t entirely new concepts for banks — but in 2018 they are gaining rapid traction across the financial services ecosystem. The power, promise, and performance, however, for Main Street banks presents different challenges and opportunities than multi-national banks.
Learn why AI and machine learning are gaining rapid traction across the financial services ecosystem in a webinar hosted by Rippleshot co-founder Canh Tran and the ABA on Tuesday, Jan. 23 at 2 p.m. EST. Click here to register for the webinar. To get a sneak peak into what we'll be discussing, download our newly published infographic.