Big Banks Cautious on AI Adoption as Startups Lead High-Risk Experiments

Big banks are adopting a cautious approach to artificial intelligence (AI) implementation, opting to let smaller, more agile startups take the lead in high-risk experimentation. Craig Corte, global head for digital, data, and coverage platforms at Standard Chartered, emphasized that large institutions should avoid being at the “cutting edge of innovation around AI” due to the reputational and operational risks involved. Instead, he argued, major financial firms can afford to lag slightly, allowing startups to test the waters and mitigate potential failures [1]. This strategy contrasts with earlier digital transformations, where larger banks were often slow to adopt new technologies, but Corte noted a reversal: today’s largest banks are actively driving AI adoption [1].

The risks associated with AI were highlighted by Tianyi Zhang of Ant International, who identified hallucinations, autonomous agent interactions, and deepfakes as critical concerns. Zhang acknowledged AI’s utility in augmenting entry-level roles, such as financial investigations, while underscoring the need for robust risk management [1]. Meanwhile, client trust in AI varies by age. Younger investors embrace AI for its speed and transparency, particularly in thematic areas like sustainability, while older clients view it as a supplementary tool rather than a primary investment vehicle [1].

Smaller startups face distinct challenges when collaborating with large banks. Vivien Jong of BNP Paribas Wealth Management shared instances where startups struggled with lengthy contracts and payment delays. One startup reportedly declined a 60-page agreement, preferring to work for free for six months, illustrating the friction between institutional processes and startup flexibility [1]. Conversely, AI offers opportunities for small businesses to access advanced tools for payments, risk management, and foreign exchange, enabling them to compete despite limited resources [1].

The discussion also touched on experimental AI-driven finance models. Michael Wu of Amber Group described “AgentFi,” a framework where AI agents autonomously manage financial decisions. However, Wu noted current limitations: agents lack the financial autonomy to execute actions independently and require human oversight. Amber Group’s first AI agent, “Mia,” exemplifies the potential and pitfalls of such systems, with Wu describing it as a “super intern” that excels in some tasks but still makes significant errors [1].

The strategic divergence between big banks and startups reflects a broader industry recalibration. While large institutions prioritize stability and risk mitigation, smaller players leverage agility to innovate. This dynamic may reshape financial services, with startups pioneering AI applications that larger firms can later adopt at scale. However, the path forward remains fraught with technical, regulatory, and cultural hurdles, particularly as AI’s role in finance evolves [1].

Source: [1] Big banks can ‘afford to be a little behind the curve’ on AI, and let smaller startups make riskier bets (https://fortune.com/asia/2025/07/29/banks-ai-standard-chartered-bnp-paribas-ant-international-amber/)