Google Cloud is encouraging financial institutions to expand their use of agentic AI, suggesting that fragmented implementations are hindering overall returns on investment. A governance-led platform approach is deemed essential for unlocking broader value across enterprise operations.
“Over the last few years, we’ve seen a lot of implementation. People have implemented AI for very specific use cases. What we’re seeing is a value gap, because these individual implementations are not getting broad enterprise value,” said Sid Nadella, Director, Financial Services Market Leader, Capital Markets, Google Cloud. He emphasized that the future will not rely on a few agents but rather on a network of “tens of agents doing different things across the enterprise,” necessitating a governance layer to oversee these applications.
This reorientation towards a platform layer aims to connect multiple agents while ensuring compliance and oversight. The operational complexities of financial institutions require these systems to integrate seamlessly across trading, risk management, compliance, and customer functions.
While Nadella acknowledged that the nature of large language models (LLMs) remains probabilistic, he pointed out that techniques like grounding and fine-tuning can enhance their explainability and relevance to enterprise data. “An agent can have deterministic skills. For example, computing portfolio risk is a deterministic calculation. You give the agent that skill so it uses an approved model rather than generating its own code,” he noted.
This hybrid approach blends probabilistic reasoning with deterministic processes, aligning with existing financial regulations. “Every bank has approved models. You’re not allowed to use an unapproved model to compute risk. Agents are going to act in the same way, using the same skills a human would,” Nadella explained.
The regulatory landscape presents additional challenges, as different geographies impose varying requirements, including data sovereignty. Nadella remarked on the rapid pace of AI development, contrasting it with the two-decade-long digital transformation in financial services. “We’ve seen exponential growth in AI capabilities over the last four years,” he stated, highlighting the tension between innovation and regulatory oversight.
For instance, in the realm of fraud detection, advancements in machine learning have significantly reduced false positives, leading to challenges with existing regulatory expectations. “When you implement machine learning, the number of false positives goes very low. Regulators are used to seeing false positives, so they think something is wrong. But the system has just become better at detecting patterns,” said Nadella. This necessitates close collaboration with regulators, as demonstrated by Google Cloud’s work with HSBC on anti-money laundering efforts, which involved extensive engagement to secure regulatory approval.
A similar collaboration is ongoing with the Chicago Mercantile Exchange, which is transitioning its core operations to the cloud. “We work closely with regulators like the CFTC. They analyse the infrastructure and certify it, so we work together to make sure it’s acceptable,” Nadella noted.
Security remains a foundational concern for financial institutions as they adopt cloud services. “Security has always been foundationally important across all our products,” Nadella asserted, adding that as AI capabilities expand, they can also be leveraged for defensive measures. “We’re bringing agentic defence that can be more proactive than reactive,” he said, indicating a shift in focus from mere compliance to a more robust security posture.
The growing interest in sophisticated financial instruments is also evident, with Nadella stating, “There’s an increased appetite to invest using sophisticated financial instruments that used to be considered complex.” However, he noted that a lack of educational resources often hampers understanding of these instruments and their associated risks. “AI is expected to play a growing role in improving financial literacy, particularly among younger investors,” he said. Even if regulatory frameworks restrict the use of AI for advice, it can still assist users in grasping complex products and personalizing the information based on their knowledge levels.
As financial institutions continue to explore the potential of agentic AI, compliance and fraud detection are seen as primary areas of opportunity, with operational efficiency in settlement processes also benefiting from these technologies. “I think compliance and fraud detection have a lot of potential, and we’re already seeing that,” Nadella remarked. While trading and risk functions remain closely tied to established models, the misconception about current capabilities and the architecture of agents may need to be addressed to facilitate broader adoption.
The evolving landscape of AI in financial services reflects not just a technological shift but a fundamental transformation in how institutions approach governance, compliance, and operational efficiency, promising a future where these capabilities are seamlessly integrated and widely implemented.
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