Financial services institutions face a critical tradeoff: they need to embrace generative AI to remain competitive, but they also operate in one of the most heavily regulated industries, where accuracy, compliance, and risk management cannot be compromised. The numbers tell the story: According to McKinsey, banks implementing generative AI can realize a potential value of $200-$340 billion annually. Yet the same institutions face astronomical costs for compliance failures, with regulatory fines in banking exceeding $400 billion since 2008. This creates an urgent question: How can financial institutions deploy generative AI at scale while maintaining the stringent oversight needed to satisfy regulators, protect customers, and prevent costly errors? Traditional approaches to AI governance rely heavily on human review, which creates three critical bottlenecks: scale limitations, consistency challenges, and speed constraints. Forward-thinking financial institutions have recognized that human oversight alone cannot scale with enterprise AI adoption. Instead, they're implementing a layered approach in which AI systems evaluate other AI systems, with humans providing strategic oversight of the process—not reviewing every individual output. Financial institutions have historically relied on straightforward evaluation metrics like BLEU and ROUGE for text-based models, but these metrics frequently fall short when applied to the open-ended, generative nature of large language models (LLMs) due to their lack of semantic understanding, inability to capture contextual nuance, and insensitivity to domain-specific requirements. LLM-as-a-Judge has rapidly evolved from a theoretical concept to essential infrastructure at leading financial institutions, using a dedicated large language model to evaluate the outputs of operational AI systems against predefined criteria, checking for accuracy, compliance, bias, and alignment with business rules. Several converging factors explain why leading FSIs consider LLM-as-a-Judge essential: regulatory expectations are evolving, the volume challenge makes automation essential, and research shows that advanced judges can match human evaluation. Galileo's approach addresses the unique requirements of financial institutions with several advanced capabilities, including ChainPoll for superior assessment accuracy, multi-judge ensemble for robust oversight, and Luna advantage for custom fine-tuned SLMs. With Galileo's proprietary, research-backed evaluation algorithms and established expertise in customizing approaches, they stand ready to be partners and strategic advisors as financial institutions enhance the reliability and efficiency of their AI systems. LLM-as-a-Judge is rapidly becoming a strategic necessity for financial institutions serious about scaling AI safely and efficiently by combining the speed and consistency of AI evaluation with strategic human oversight, enabling innovation while satisfying regulatory demands.