across organizations, from customer service enhancements and fraud detection to process automation and predictive analysis. Key to all these areas is AI’s potential to remove barriers between banks and their customers. For example, AI can sift through reams of customer correspondence via emails and online chats, isolating recurring complaints and tailoring operational changes accordingly.
Machine learning has provided businesses more insight into customer behavior than they've ever had. With the, for instance, banks can use predictive models to target high-value customers who may be about to take their business elsewhere, cuing the bank to step in and take action to keep them happy and loyal, before it’s too late.
“A lot of what we’re seeing with machine learning focuses on predictive models, being able to analyze past behavior and then predict things such as future customer behavior, possible churn indicators and propensity to buy,” says Sifter.By now, most consumers have encountered Natural Language Processing through the pervasive virtual assistants that operate in their homes and on their smartphones.
Many banks see RPA as a promise of efficiency and reduction in human error, especially in cases like “the processing of a loan application or [customer] requests,” says Sifter. It offers “increased optimization of processes and workflows.”
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