How to Build Compliant AI Agents for Banking: A Developer's Architecture Guide

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A software developer on DEV Community has outlined a practical architecture for building AI agents that can pass banking compliance audits, using loan underwriting as a case study. The guide emphasizes that roughly 70% of the effort in fintech AI projects goes into compliance infrastructure — including audit logging, explainability, and human-in-the-loop checkpoints — rather than the AI logic itself. A core requirement highlighted is that every agent decision must be fully traceable, with reasoning steps recorded in real time rather than reconstructed after the fact. The article includes working Python code demonstrating an 'AuditableDecision' class designed to capture inputs, reasoning chains, data sources, and human-readable explanations for each decision. The guide is aimed at developers who find that real-world regulated AI deployments take significantly longer than prototypes suggest.

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