Financial institutions use artificial intelligence (AI) largely for fraud prevention, but the technology is increasingly moving into their consumer applications. One industry report notes that 56 percent of the AI products used by banks are for risk-related applications, such as cybersecurity, compliance, and loans; 25 percent is used for customer-facing functions, such as customer service and marketing. While conversational interfaces, such as chatbots, comprise only 13.5 percent of all bank-related AI products, they account for nearly 40 percent of all bank-related AI use cases.

AI-enabled apps engage customers and can handle some of the more basic customer concerns. One leader in AI interfaces for financial institutions is the Kasisto chatbot, based on the KAI platform. KAI allows companies to build virtual assistants that can communicate with customers over multiple messaging platforms. It uses conversational AI technology to enable more natural interactions with customers, and banks can customize the platform using APIs. The Kasisto platform is behind the virtual assistants for several institutions, including DBS Bank, J.P. Morgan, Standard Chartered Bank, TD Bank Group, and Wells Fargo.

Wells Fargo’s Kasisto-based chatbot helps customers pay bills, check account information, and assess spending and budgeting habits. Through machine learning, banks can automate responses to frequent queries and identify potential problems based on clusters of similar complaints. That frees bank staff for more complex interactions.

Image credit: Kasisto

Predictive Analytics Based on Pattern Analysis

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.

AI Draws Customers to Additional Products

Improving customer service is a key goal, but enticing customers to explore new financial products translates to more money for the banks. AI can generate new business from existing customers.  

J.P. Morgan is using machine learning to identify clients in its Equity Capital Markets arm that might be good candidates for additional equity offerings. The institution is considering the use of predictive analytics in other areas as well.

US Bank employs predictive analytics in its Expense Wizard digital assistant. Designed for business travelers, the expense management app allows companies to give its employees access to a virtual credit card that ties into expense reporting software. Travelers use the card to book tickets, hotels, and related expenses without paying upfront, and companies get a detailed accounting of employee expenses. 

AI technologies and predictive analytics provide banks an extension of their customer service capabilities, but the real value lies in tailored financial guidance for customers and an introduction to additional banking services.

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