Traditional banks are falling behind fintech firms in adopting artificial intelligence (AI), especially in areas that drive revenue and enable autonomous operations, according to McKinsey & Co.
Based on analysis of 4,000 fintechs worldwide, McKinsey reported that these firms account for nearly 70% of AI deployments. Banks, however, continue to grapple with stricter regulations, outdated and siloed systems, and internal resistance to change.
While fintechs are already scaling AI across analytics, trading, and portfolio management, many banks remain stuck in the pilot stage—experimenting with promising ideas but struggling to fully implement them.
For banks, the strongest growth is currently seen in agentic AI and revenue-focused tools like AI-driven multi-asset trading systems and predictive decision intelligence. Japan’s SMBC, for example, has set up a Singapore-based startup to rapidly test agentic AI without lengthy approval processes.
Most traditional institutions continue to concentrate on trust-building and operational improvements through solutions such as treasury automation, conversational banking, and personalized wealth advice. McKinsey warned that these limited applications risk becoming commoditized and could prevent banks from realizing AI’s full potential.
Southeast Asia’s DBS has launched a generative AI assistant for corporate clients, while OCBC rolled out an AI-enabled stock advisory tool trained on 4,000 equities.
Fintechs, on the other hand, are aggressively expanding into agentic AI for predictive analytics, sophisticated trading, and real-time financial insights. One AI-backed trading platform cited by McKinsey enhances returns through real-time algorithmic decision-making.
Ant International has integrated an autonomous travel assistant into its Alipay+ Voyager digital wallet, helping users plan and book trips and purchases. Stripe is likewise experimenting with agentic AI to reinvent the entire online payment experience, from browsing to checkout.
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