Google’s Practical AI Playbook for Banks and Fintechs

Karen Zhang explains how Google is supporting organisations across financial services, from small fintechs to large financial institutions. The common theme throughout the conversation is using AI in practical ways: improving customer experiences on the front end and reducing repetitive workload on the back end, so teams can focus on work that needs real judgement.

Zhang highlights a partnership with Starling Bank to create a “Spend intelligence” service. Put simply, it lets Starling customers ask natural-language questions inside the app (typed or voice) and get clear answers about their spending. Instead of digging through statements and filters, users can ask things like: “How much have I spent on TFL and transport over the last week?” or “Has that changed week on week over the last month?”  The point is to make spending insights feel more like a conversation, and easier to access for everyday users.

For fintech teams, Zhang’s example also signals a shift in product thinking. Natural-language interfaces lower the barrier to insight as customers don’t need to know where to tap or how to interpret charts to find what they need. Done well, this supports budgeting, spotting patterns, and noticing gradual changes in behaviour, without turning the user into a data analyst.

Zhang then moves to internal automation, using a second example with Liberis who Google partnered with to build an AI underwriting agent called Ada, named after Ada Lovelace. Underwriting often involves large volumes of information and repeatable steps, which can create heavy admin load. According to Google, Ada works alongside underwriters, helping them through the process and reducing overhead by 50%. Zhang frames the benefit as both efficiency and focus: AI takes on more repetitive tasks, while underwriters spend more time on higher-stakes, knowledge-based decisions.

Google finishes with a scaling message which is while these examples sit in the mid-tier fintech space, the same approach can apply to much smaller firms. The idea is that with the right AI support, teams don’t need huge headcount, “100 underwriters,” as Zhang puts it, to deliver strong service. For banks and fintechs trying to balance cost and customer experience, Google’s point is straightforward: use AI to remove friction for customers and cut repetitive work internally, while keeping human judgement where it matters.