AI Credit Analytics Across Amazon S3 and Snowflake with Amazon Bedrock AgentCore

This article presents a deployable reference architecture using Amazon Bedrock AgentCore to orchestrate AI-powered credit analytics across unstructured documents in Amazon S3 and structured data in Snowflake, with enterprise-grade security and auditability.

• AgentCore orchestrates three MCP-backed tools: knowledge base search over policy PDFs, semantic search across customer profiles, and natural-language-to-SQL execution against banking data
• Per-user identity flows through three-legged OAuth with Okta, ensuring every query appears under the analyst's name in audit logs, meeting regulatory requirements
• Cedar policies enforce role-based access control at the Gateway layer; Amazon Bedrock Guardrails automatically redact PII from responses
• Full loan eligibility assessment completes in ~70 seconds versus 20-40 minutes of manual cross-referencing, delivering 17-34x improvement
• MCP standard enables extensibility: adding new data sources (Redshift, RDS, DynamoDB) requires only configuration, not re-engineering
• Reference implementation includes one-click AWS CDK deployment, synthetic banking data, and complete agent code on GitHub

The architecture demonstrates how to balance intelligent automation with regulatory compliance in financial services through standardized protocols, enterprise identity, and policy-driven governance.