Generative AI Customer Example: Ubiquity-AI – Simeone GPT Bot

Case Studies May 13, 2025

1. Business Problem

Debt collection in the banking sector often occurs late in the credit lifecycle—typically after customers have defaulted or are unreachable. This results in increased operational costs, high default rates, and damaged customer relationships. Traditional debt collection processes rely heavily on human agents and static rule-based systems, leading to inefficiencies, missed recovery opportunities, and poor customer engagement.

2. Project Objective

To deploy a proactive, AI-powered chatbot—Simeone GPT—that assists the debt collection department by:

• Identifying which customers are likely to repay their outstanding debt.
• Reaching out automatically to those customers before they default.
• Offering flexible installment-based repayment options.
• Reducing the need for manual agent intervention.
• Maintaining a positive customer experience and preventing negative credit records.
  

3. Business Problem and Traditional Approach

Traditional Approach Challenges:

• Manual Risk Assessment: Debt repayment likelihood determined manually or with basic scoring tools.
• Delayed Engagement: Outreach occurred after default or customer disengagement.
• High Agent Dependency: Required significant agent time for each customer.
• Poor User Experience: Customers had limited self-service options for repayment.
• Lack of Predictive Insights: Inability to forecast repayment intent accurately.
  

4. Generative AI Solution on AWS

Solution Architecture

The Simeone GPT bot was built using a serverless and scalable architecture powered by AWS Generative AI services. It performs predictive analytics, user engagement, and conversational follow-ups autonomously.

• Frontend Integration: Secure bank portals and mobile apps integrated with chatbot UI.
• Amazon API Gateway: Handles secure API calls between frontend and backend services.
• AWS Lambda: Stateless compute for all business logic including customer outreach and decision trees.
• Amazon RDS (MySQL): Stores structured customer debt and repayment behavior data.
• Amazon Bedrock (Llama Model): Powers intelligent conversations and repayment options.
• Amazon Lex: Enables natural language conversations with customers.
• Amazon SNS: Triggers real-time alerts and reminders to users and debt agents.

AWS Generative AI-Specific Services Used

• Amazon Bedrock (Llama Model): Generates dynamic and empathetic response flows for sensitive topics like debt.
• Amazon Lex: Creates conversational flows for negotiation and installment setup.
• AWS Lambda: Processes all logic around scoring, outreach, and chat workflows.
  

5. Quantitative Business Impact Metrics

6. Business-Led Transformation Using Generative AI

• Predictive Recovery Engagement: AI determines customer repayment probability before default.
• Proactive Outreach: Bot initiates early intervention via conversational interfaces.
• Self-Service Repayment: Customers can negotiate repayment terms, dates, and frequency.
• Reduced Manual Overhead: Agents only step in for complex or escalated cases.
  

7. Deployment and Production Readiness on AWS

• Deployment Environment: AWS Cloud
• Frontend Hosting: Integrated with bank’s internal portals and customer apps
• Production Status: Fully deployed, integrated with CRM and debt systems
• Scaling Strategy: Fully serverless with Lambda auto-scaling and Lex concurrency
• Security Measures: IAM roles, API Gateway throttling and authorization, AWS WAF, KMS encryption for sensitive data
  

8. Conclusion

The Simeone GPT Bot, deployed by Atomic Computing for Ubiquity-AI, demonstrates how Generative AI on AWS can transform debt collection from a reactive, high-cost process to a proactive, customer-centric solution. Leveraging Amazon Bedrock, and Lex, the chatbot engages customers with empathy, improves repayment success, and preserves their financial reputation—while reducing operational burdens on the bank.