The financial industry has embraced artificial intelligence (AI) with remarkable speed. Algorithms approve loans in seconds, chatbots resolve complaints around the clock and fraud detection systems process billions of transactions daily. By almost every operational metric, AI is delivering. Yet there is a growing risk that the industry is measuring the wrong things. The dominant narrative — centered on cost reduction and automation — is dangerously incomplete. AI in financial services must be reframed, from an efficiency tool to a trust infrastructure. That reframing has implications across customer service, product development, and security. Customer service: The efficiency trap The most common measure of AI success in customer service is cost. McKinsey estimates that AI-driven automation could reduce customer service costs in banking by up to 30 percent. This is a legitimate gain — but institutions that optimize purely for cost reduction risk hollowing out the relationship that banking fundamentally depends on. The contrast between two approaches is instructive. When Wells Fargo
[Economic Essay Contest] Asymmetric realities, divergent solutions: Comparing AI governance in Korean and Mexican financial inclusion