Financial institutions are turning to AI more and more as of late for assistance in advising their customers. Artificial intelligence platforms have long been used for improving customer service, security, and revenue. Today, predictive analytics is analyzing customer spending and saving habits and recommending ways for them to improve their financial position. It should come as no surprise that many subsequent recommendations include the introduction to ancillary products and services offered by the bank. The article that follows does an excellent job of describing how real world platforms are being used for this purpose in banks and financial institutions today.
More than simply answering questions, banks are using machine learning and predictive analytics to improve customer finances and avoid problems. At Wells Fargo, AI-enabled apps analyze customer spending and then suggest ways to save money based on those patterns. The platform can categorize transactions by date or recipient and remind customers to pay a bill or transfer funds if an account is low. Anomaly-detection capabilities flag higher-than-normal recurring payments and double charges.
Predictive analytics works on a larger scale as well. An intelligent platform can review customer finances, crunch numbers, and suggest the most effective ways to reduce debt, such as make an extra loan payment this month or pay down a credit card bill, or add to savings. The software can even instruct customers with savings accounts on how to earn higher interest rates.