GenAI-Powered Text-to-SQL Automation for Consumer Loan Analytics

A large financial services organization offering consumer loans manages vast volumes of structured data related to loan applications, customer profiles, repayments, and risk assessments. While data played a critical role in daily operations and compliance, access to insights was largely dependent on technical teams capable of writing complex SQLqueries

The Challenge As lending operations scaled, business users across credit, operations, and complianceteams faced several challenges 

•  Heavy dependency on data analysts for routine queries
•  Delayed decision-making due to SQL query backlogs
•  Limited ability for non-technical users to explore data independently
•  Risk of performance issues from ad-hoc query execution
• Reduced analytical agility and slow trend monitoring

 The organization needed a secure, governed way to democratize access to data withoutcompromising performance or accuracy.

Solution Approach Pentagon designed and implemented a GenAI-powered Text-to-SQL platform on AWS,enabling business users to query databases using natural language. The solution allows users to ask questions such as “How many loan applications were rejected last month due to low credit score?” and receive accurate, governed responses without writing SQL. Architecture Highlights

•  Amazon EC2 hosted the chatbot application and enterprise integrations
•  Amazon Bedrock processed natural language queries using foundation models
•  Amazon RDS acted as the authoritative data source
•  Schema-aware grounding ensured only optimized, read-only SQL queries were generated
• Built-in governance rules validated queries before execution

Business Outcomes 

• 50% reduction in dependency on data analysts
•  60% faster access to insights
•  40% increase in data usage by non-technical teams
•  30% reduction in query-related errors
•  25% improvement in decision turnaround time