Shuchi Agrawal: Finance & AI leader "Breaking the Binary" by reimagining legacy banking through human-centric, autonomous systems.
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Every bank wants an AI strategy. Far fewer have an AI‑ready bank.
In wholesale and corporate credit, a familiar pattern exists: world‑class people making billion‑dollar decisions on top of brittle systems that were never designed for the speed, complexity and scrutiny they now face. The issue isn’t a lack of models. It’s the architecture they sit on.
Early in my career, leading AI and data initiatives in global banking, I watched deals die in rooms that had nothing to do with pricing or risk. They failed because onboarding systems couldn’t communicate, and APIs didn’t exist. Data sat in siloed platforms, producing multiple sources of truth and no real confidence. Revenue didn’t just slip away; it evaporated.
On the risk side, the story was similar. Teams were talented and well‑intentioned, but the technology couldn’t deliver a complete, timely view of exposure. That’s where you see the downstream effects: delays, remediation work and regulatory findings that trace back not to malice, but to plumbing.
Meanwhile, major industry research now highlights AI and data as core drivers of banking’s next performance curve, from profitability to productivity. The leaders aren’t just piloting models. They’re rebuilding the pipes.
For decades, wholesale credit ran on a simple assumption: You could treat data like a file that is captured, stored and retrieved when needed.
That assumption is now broken.
Markets move faster. Client expectations are higher. Risk events propagate across portfolios and geographies in days, not quarters. Supervisors are watching how AI, outsourcing and data concentration change operational and systemic risk. In that environment, a static view of credit is an accident report, not a control mechanism. As Deloitte reports, “The biggest challenge for firms is less about dealing with completely new types of risk and more about existing risks either being harder to identify in an effective and timely manner, or manifesting themselves in unfamiliar ways.”
Across the industry, a common theme has emerged: Value is shifting toward institutions that use data and AI to refresh risk views continuously, not periodically. In practice, that means:
• Data that updates in (or near) real time
• Early warning signals that flag anomalies before they become findings
• Pricing, limits and terms that can be adjusted dynamically, not just at annual review
The shift isn’t “more dashboards.” It’s treating credit as a living system.
This is where many banks get stuck.
Hundreds of millions of dollars are poured into cloud programs. Infrastructure improves. PowerPoints signal transformation. But fragmentation, data silos and conflicting taxonomies simply migrate to a more modern environment. According to McKinsey & Company’s Global Banking Annual Review, “Banks spend about $600 billion a year on technology but productivity remains low.”
Industry voices have been explicit: You cannot unlock the full value of AI in banking without getting the data and architecture right. Cloud is an enabler, not a strategy. If your operating model still depends on manual reconciliations, Excel bridges and black box tools, generative AI on top will amplify noise as much as it creates insight.
The result is a familiar frustration for executives: The spend is real, but the business impact feels incremental.
Transformation doesn’t start with another model. It starts with how you design the bank around that model:
If there is no single, dependable foundation for client, exposure and market data, every AI use case becomes a custom project. Many leading banks are focusing on domain‑based data architectures that make critical data products reusable across risk, finance and the front office. This is unglamorous work, and it is the work that makes everything else possible.
The next wave won’t just be chatbots for customers. It will be AI agents coordinating workflows across systems: gathering documents, reconciling discrepancies, suggesting terms and generating committee materials. That only works if your architecture lets systems share context in a governed, auditable way.
Emerging agent‑to‑agent standards with protocols, such as MCP (Anthropic) and A2A (Google), and supervisory guidance on AI are pushing in the same direction: interoperability with control. The winners are doing more than creating agents. They’re building governance frameworks that scale.
The best first use cases are rarely the flashiest. They are early‑warning and monitoring scenarios: catching stale data, surfacing limit breaches, highlighting inconsistent client records or flagging outlier transactions before they hit a regulator’s radar. These are the systems that prevent losses, findings and reputational damage, and they build organizational trust in AI far faster than abstract optimization.
Supervisors are becoming increasingly aware that organizational structure and governance are just as important as technology. The transformations that stick have one thing in common: Risk and technology leadership jointly own the platform. When the CRO and CIO are jointly accountable for the data and decision core, silos become design problems to solve rather than political realities to work around.
The next divide in banking won’t simply be between AI adopters and everyone else. Most institutions will adopt AI in some form. The real gap will be between two types of banks:
• Banks that have clean, connected, governable plumbing
• Banks that keep adding smart endpoints to a fundamentally fragile system
The first group can remove friction from onboarding, continuously refresh credit views and respond to risk events before they become headlines. They will be able to redeploy human capacity away from reconciliation and toward higher‑value work.
The second group will keep buying tools and hiring talent, and still spend board meetings asking why transformation isn’t showing up in cycle times, cost ratios or regulatory outcomes.
Wholesale credit has powered global banking for a century. Now the engine is being rebuilt, often in real time, under regulatory spotlights and competitive pressure. The winners won’t be the banks with the fanciest models.
They’ll be the banks that finally fix the plumbing.
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