Wells Fargo has begun a number of AI initiatives, some they’ve created in-house and some they’ve created with help from vendors. In this article, we detail the following AI initiatives at Wells Fargo:
- Conversational AI with Kasisto: Wells Fargo’s chatbot application built on an AI platform from its incubator project.
- Predictive Analytics tool for Mobile Banking: The bank’s new feature for its mobile app, which allows customers to see money-saving recommendations and an analysis of their spending habits.
We begin our exploration of Wells Fargo’s AI initiatives with its collaboration with Kasisto for a customer service chatbot.
Conversational AI with Kasisto
According to Deloitte, AI is a technology that will change how financial markets are structured and regulated. Perhaps it already has, given the numerous AI initiatives at some of the largest banks. Wells Fargo has purportedly been using AI to handle customer service questions since 2017. At the same time, the AI startup Kasisto was in the middle of Wells Fargo’s Startup Accelerator Program.
Wells Fargo worked with Kasisto to develop its first customer service chatbot. Although Wells Fargo doesn’t make that much information about their partnership with Kasisto available, Kasisto claims its chatbot platform KAI could help banks create chatbots that could:
- Allow customers to ask questions, make payments, and review account balances and details.
- Provide customers with information about the status of their loan applications.
- Fulfill certain requests directly within the conversational interface. such as sending and scheduling payments.
The following 3-minute demonstration shows a KAI chatbot made for Mastercard which can handle customer questions about personal data such as account balances:
Predictive Analytics Tool for Mobile Banking
Wells Fargo also offers an AI-powered predictive analytics tool in its mobile banking app. The bank claims its customers can use this tool to see an analysis of their spending habits and receive recommendations on how to save money based on those habits.
The tool purportedly facilitates financial planning based on spending data from a six month period and recommends how to save for upcoming bills.
The 1-minute video below is a showcase of some of the predictive banking tool’s capabilities. The video shows what the user interface looks like on a smartphone with example recommendations and analytics:
The chatbot likely leverages predictive analytics because it can make predictions of what will happen in the future based on a backlog of historical data. The machine learning algorithm the software is based on would need to be trained on large amounts of customer spending data in order to recognize trends and how they might change.
Over time, the algorithm would improve at predicting a customer’s future financial state and recommending money-saving methods.
For example, Wells Fargo may set the money-saving recommendations on a monthly time scale so that a customer may find ways to have more money at the end of each 30 day period.
The software would then analyze a given customer’s financial history to look for trends and spending habits. It could then recommend ways to save based on these habits.
The software would ideally make this recommendation based on a measurement that would bring the customer closer to a net positive at the end of the month.
Header Image Credit: PYMNTS
