Snowflake-Labs/sfguide-retail-banking-credit-card-and-loan-analyst

Snowflake-Labs/sfguide-retail-banking-credit-card-and-loan-analyst

License: Apache-2.0

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Created: 2026-01-30T19:24:38Z

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README:

Retail Banking Credit Card and Loan Analyst

Overview

This guide demonstrates how Snowflake Cortex Agents with Semantic Views enable conversational analytics across auto loans and credit card portfolios for retail banking. You'll build a Consumer Bank Agent that can answer natural language questions spanning two lines of business.

What You'll Learn

• How to create Semantic Views that define business-friendly data models
• How Cortex Analyst translates natural language into accurate SQL queries
• How Cortex Agents orchestrate multiple data sources to answer complex questions
• How to use Snowflake Intelligence for AI-powered analytics

What You'll Build

| Component | Description | |-----------|-------------| | Database | BANKING_DB with AUTO_LOANS and CREDIT schemas | | Semantic Views | Two semantic views for auto loans and credit card data | | Cortex Agent | Multi-LOB agent (CONSUMER_BANK_AGENT) for conversational analytics | | Sample Data | Customers, loans, payments, vehicles, credit cards, transactions |

Deployment

1. Run Setup Script: Open scripts/setup.sql in a Snowflake SQL Worksheet and execute all statements 2. Open Snowflake Intelligence: Navigate to Snowflake Intelligence in Snowsight 3. Select the Agent: Choose Consumer Bank Agent and start prompting with the example questions below

Example Questions

Auto Loans:

• "Total loans and total principal by month"
• "Show me customers with late or missed payments"

Credit Cards:

• "Active cards, average credit limit, and average APR by product"
• "Show transaction volume by merchant category"

Cross-Portfolio:

• "Customers with both an active auto loan and an active credit card"
• "Total exposure per customer (loan principal + card balance)"

License

Copyright (c) Snowflake Inc. All rights reserved.

Licensed under the Apache 2.0 license.