The rise of agentic AI in BFSI: From static systems to self-directed intelligence

By Deviprasad Thrivikraman Pillai, Managing Director, Zentis AI

For years, the BFSI industry has relied on automation to manage scale and efficiency. Robotic Process Automation (RPA) reduced manual work, while machine learning brought intelligence to specific tasks such as credit scoring, fraud detection, or customer service. These approaches delivered results, but most implementations were narrow, fragmented, and difficult to scale across complex enterprise processes.

Typically, automation in BFSI meant stitching together scripts, rules, and models to keep pace with scale. However, customer expectations, regulatory complexity, and market volatility are now moving faster than static systems could adapt.

The rise of Agentic AI is possible today because several forces have converged:

• Foundation models: Advances in large language and multimodal models give agents reasoning ability beyond traditional Machine learning classifiers.
• Agent orchestration frameworks: New architectures allow multiple agents to collaborate across workflows, not just execute isolated tasks.
• Open data ...