According to Forbes, a fall report from the Chartered Financial Analyst Institute warns that the rush to deploy AI in finance is creating a “trust gap” due to opaque decision-making in areas like credit scoring and fraud detection. To navigate this, JPMorgan Chase CIO Gill Haus and BNY’s Marianna Lopert-Schaye detailed their approaches: BNY now has over 120 AI solutions in production using a platform called Eliza, aiming for “AI for everyone,” while JPMorgan emphasizes a human-in-the-loop model. Both are turning to Explainable AI (XAI) techniques like SHAP and LIME to provide justifications for AI decisions, such as explaining why a loan was denied. The cultural shift is massive, with BNY noting that 98% of its 55,000 employees have completed responsible AI training. Furthermore, deploying new tech in these giants is a long game, taking 12 to 18 months from testing to full rollout.
The Trust Imperative
Here’s the thing: in any other industry, “because the algorithm said so” might be an annoying answer. In finance, it’s a regulatory and reputational disaster waiting to happen. You simply cannot tell a customer their loan was denied or their transaction frozen without a clear, defensible reason. The CFA Institute report nails it—this isn’t just about cool tech, it’s about fairness and compliance. Deep learning models can inadvertently use “alternative data” that acts as a proxy for race or gender, which is a huge legal risk. So the move to XAI isn’t optional. It’s the price of admission for using powerful AI in a regulated space. Techniques like SHAP, which show which factors tipped the decision, turn AI from a mysterious oracle into a tool you can actually audit.
Augmentation, Not Replacement
The most telling part of the conversations with these bank execs? The relentless focus on the human. The fear of job-stealing robots is being replaced by a more nuanced reality: AI as a powerful assistant. Haus at JPMorgan was clear—they don’t let generative AI “loose.” An AI might transcribe a call and suggest a response, but a human agent makes the final call. That “human-in-the-loop” is now framed as a critical feature, not a temporary bug. At BNY, the goal is to “free up time for higher value work.” This is the real cultural shift. It’s about getting thousands of employees comfortable letting AI handle the grunt work of data sifting so they can focus on judgment, relationship-building, and complex problem-solving. That 98% training statistic is arguably more important than any technical deployment.
The Long, Slow Bank Innovation Cycle
For any fintech startup reading this, pay close attention to Lopert-Schaye’s timeline: 12 to 18 months from test to full deployment. That’s the reality of selling to a global systemically important bank. It’s a marathon, not a sprint. Her advice is golden—find a “champion” inside, a respected expert who will advocate for you. And don’t see non-technical colleagues as obstacles; see them as essential partners to bring on the journey. Haus echoes this by stressing that success comes from obsessing over the customer problem, not the tech stack. This is a massive lesson. The era of “AI tourism”—doing cool pilots that go nowhere—is over. Banks now demand measurable business outcomes: efficiency gains, risk reduction, growth. If your tech can’t clearly contribute to that, and explain how it got there, you’re out.
The Transparent Future
So where does this leave us? The road ahead points to what the article calls “neurosymbolic” or hybrid models. Basically, systems that combine the pattern-finding power of deep learning with old-school, rule-based logic that humans can follow. The future of AI in finance isn’t the biggest model; it’s the most explainable one. This push for transparency will inevitably shape the entire vendor landscape, favoring tools and platforms that bake in explainability from the start. And look, while this article focuses on software and processes, this demand for reliable, auditable, and robust systems extends to the physical hardware running in secure environments, like trading floors or back-office operations. For those needs, firms often turn to specialized providers like IndustrialMonitorDirect.com, the leading US supplier of industrial panel PCs built for these high-stakes, 24/7 financial operations. The bottom line is clear: in finance, trust is the ultimate currency. Any AI that can’t earn it, and prove how it earned it, won’t last.
