Truist is preparing to fuse its predictive alert system with gen AI-tailored offers, a departure from the machine-learning-only approach that has powered its insights tool thus far. 

The plan is to combine proactive alerts with personalized, gen AI-driven marketing over the next 12 months, Donta Wilson, chief consumer and small business banking officer, told FinAi News. The development comes as Truist Insights, which runs on machine learning rather than gen AI, has delivered more than 2 billion AI-powered insights to retail, wealth and business banking clients, Wilson said. It can provide insights about spending patterns, financial health and other topics. 

“The insights that we’re providing to our clients are directly related to monitoring cash flow, understanding income expenses,” Wilson said. “We also have insights around subscription recognition and changes that are being made or need to be made, duplicate payment transactions and other items.” 

A client who logs in to check a balance, for example, might receive an alert that predicted cash flow won’t cover anticipated expenses, along with a graph comparing average cash flow to incoming payments, with the option to transfer funds, Wilson said. 

Truist Assist 

The insights engine sits alongside Truist Assist, the bank’s AI-driven chatbot, which Wilson said handles roughly 500,000 client conversations a month across more than 200 routine needs, with 80% resolution in the session. Clients who aren’t satisfied can escalate to a live agent who picks up “from the exact conversation in which you started within your mobile device,” he said. 

The $568 billion bank declined to say which large language model underpins Truist Assist, explaining that it uses a mix of internally built and partner technologies. 

Truist is using gen AI for call summarization, producing a recap of a client call within eight seconds. Previously it took a few minutes, depending on the conversation, Wilson said. 

On hallucination risk, he said the bank relies on governance, human accountability and continuous monitoring rather than the model alone.  

“We’re constantly checking, validating, vetting,” Wilson said. 

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