From Manual Reports to Generative and Agentic AI Automation in Finance – with Pavlé Sabic of Moody’s

• Agentic AI reduces credit memo production time by 60% while keeping humans in control
• Proprietary data beats internet scraping for regulatory compliance and audit requirements
• Financial institutions use AI assistants, not autonomous decision-makers, for high-stakes workflows

The financial sector's cautious embrace of agentic AI reveals a mature approach to automation that prioritizes compliance over speed. While tech companies rush to deploy autonomous systems, banks are taking the sensible route of keeping humans firmly in the driver's seat. This measured approach makes perfect sense when your mistakes can trigger regulatory investigations rather than just angry tweets.

Moody's strategy of leveraging proprietary data highlights a key advantage that established financial institutions hold over AI-native startups. When your AI agent can draw from decades of credit research and risk analytics rather than whatever it scraped from Reddit last week, the quality gap becomes substantial. This data moat could prove decisive as regulatory scrutiny intensifies and audit requirements multiply.

The 60% reduction in production time while maintaining human oversight represents the sweet spot for enterprise AI adoption. Rather than chasing the fantasy of fully autonomous systems, Moody's has found a practical middle ground that delivers real productivity gains without sacrificing accountability. This human-in-the-loop approach may seem less exciting than sci-fi visions of AI takeover, but it's probably the path most enterprises will follow as they discover that boring reliability beats flashy autonomy in regulated industries.