NorthRiver Bank — Real-Time Credit Decisioning

NorthRiver Bank needed to modernize loan approvals to cut turnaround times while ensuring compliance. Traditional processes were manual, error-prone, and slow—leaving customers waiting days for credit decisions.

Key Challenges:

Slow approvals — Manual review delayed loan offers and frustrated applicants.
Inconsistent compliance — Policies applied unevenly across teams created audit gaps.
Data silos — Application, bureau, and KYC data scattered across systems.
Expensive manual work — Human analysts spent time on low-risk, repeatable reviews.
Limited explainability — Decisions lacked transparent rationale for regulators.

Proposed Architecture

Sources → Confluent Cloud | Kafka

Loan application, bureau data, and KYC updates stream into Confluent Cloud.
Schema Registry ensures standardized, governed formats.

Streaming transforms → Flink on CFK (Kubernetes)

Flink enriches loan applications with bureau scores and debt-to-income ratios.
Results published as curated topics (e.g., gold.loan_underwriting).

AWS Integration → Kinesis + Lambda

Flink writes enriched events into Kinesis.
Lambda triggers on each event, normalizes into a case, and invokes Bedrock Agent.

Agentic Decisioning → Agents for Bedrock

Bedrock Agent references a Knowledge Base of regulatory policies.
Produces Approve / Decline / Escalate decisions.
Escalated cases trigger Salesforce or case management APIs.

Analytics & Audit → S3 + Iceberg + Glue

All agent outputs logged in Iceberg tables.
Dashboards in Snowflake/Trino provide regulators and executives with a full decision trail.

Why Agentic AI?

Faster loan approvals (minutes instead of days).
Compliance consistency through codified policy enforcement.
Audit-ready with every decision stored alongside rationale.
Analyst efficiency by focusing humans on edge cases, not routine approvals.