A Case Study
AI-powered collection chatbot for 24/7 customer self-service
We built a secure conversational AI assistant (chatbot) that helps account holders check balances, make payments, and manage payment plans while targeting reduced routine support calls and increased digital payment adoption.
Industry
Lending
Technologies Used
- Django 5.2.8 with Django REST Framework
- PostgreSQL
- Redis (session buffering and caching)
- Bootstrap 5 + JavaScript (portal UI)
- Claude Sonnet 4.6 large language model
- Secure AI tool-calling architecture
- REST API integrations
- PCI-compliant payment integration
Project Solution
We built a conversational AI assistant embedded directly within the client’s debtor self-service portal.
The solution enables authenticated customers to check balances, review payment history, make payments, set up installment plans, update contact information, and access account support through natural language conversations.
The assistant connects with the client’s collection management, account, CRM, and payment systems to retrieve verified information through controlled workflows. Sensitive data is sanitized before reaching the language model, while escalation workflows identify disputes, hardship situations, legal concerns, and other cases requiring human support.
Client Overview
The client is a B2B software company that builds debt collection and accounts receivable management solutions for collection agencies and organizations handling receivables.
Their platform manages customer accounts, communications, payments, reporting, and compliance, and includes integrated telephony, an online customer portal, and artificial intelligence capabilities.
The client wanted to improve customer self-service capabilities while reducing the operational workload created by repetitive account-related inquiries.
Many inbound requests involved routine questions about balances, payment history, due dates, and payment arrangements. These interactions required access to accurate account information but often did not require agent intervention.
The Solution
We developed a chat-based AI assistant that connects a conversational interface to the client's existing account, payment, and CRM systems rather than building a parallel system.
The client's collectors were handling approximately 15–20 calls per day, many involving routine questions such as account balances, due dates, and payment-plan options. The solution targets reducing these routine calls to 1–5 per day, allowing collectors to focus on cases that require human judgment.
The AI assistant does not generate account balances or payment figures. Instead, it retrieves verified information from connected systems through approved tools and returns the results to the customer.
Every interaction is logged to support traceability and compliance requirements.
When conversations involve disputes, hardship situations, legal concerns, emotional distress, or other exceptions, the automated flow stops and a live collector receives the conversation history so customers do not need to repeat their information.
What makes this AI assistant different
This AI assistant was designed for financial conversations where accuracy, security, and compliance matter.
We built this AI assistant as a governed self-service layer connected to the client’s existing collection and payment systems.
Connected to real account data
The assistant does not rely on static responses or stored FAQs. It retrieves approved account information through secure integrations, allowing customers to access current balances, payment history, transaction details, and payment options through conversation.
Designed for financial accuracy
The assistant uses controlled tool calling to retrieve verified information from backend systems instead of generating account details independently.
Security built into every interaction
Customer information is processed through security controls before reaching the AI model. Sensitive data such as SSN, date of birth, and card information is sanitized, while system actions and interactions are logged for traceability.
Automated support with human oversight
The assistant handles routine account requests while identifying situations that require human support. Complex cases are escalated with conversation history available to collection specialists.
Built for expansion across channels
The solution was designed as a web-based self-service experience with the ability to extend the same governed AI approach to mobile, SMS, messaging, and voice channels.
The Approach
We built the assistant as an orchestration layer between the customer-facing chat experience and the client's existing account, CRM, and payment systems, treating security and escalation logic as first-class design requirements rather than an add-on.
Customer asks → AI understands → Security checks → Approved tools retrieve data → Customer receives answer → Human escalation when needed
Key elements of the approach included:
Redacting sensitive fields such as SSN, date of birth, and card numbers before customer messages reach the language model
Running escalation checks before automated responses are provided
Building governed prompts with policy rules, authenticated account context, and approved tool definitions
Retrieving balances, transaction history, and account data through authenticated APIs
Enforcing backend validation on payment amounts before transactions proceed
Generating secure payment links to move customers from balance inquiry to payment
Logging sanitization steps, tool calls, data retrieval, and responses for audit traceability
Designing controls aligned with FDCPA, PCI DSS, and SOC 2 requirements
Target Business Impact
The AI-powered collection chat assistant was designed to improve how customers interact with collection services by providing immediate access to account information and payment support.
The solution establishes a self-service channel that helps reduce routine inquiries, improve response times, and support collection teams with better-qualified escalations.
The implementation was built around measurable business objectives:
50-70%
Target reduction in routine customer service calls
24×7
Self-service availability
60-70%
Target self-service completion
25-40%
Target increase in digital payment adoption
Under 30 seconds
Target reduction in routine balance inquiries
Production deployment was pending at the time of documentation, so these figures represent defined success targets and measurement criteria rather than completed production results.
Final Words
AI-powered chat assistants can help collection organizations create faster self-service experiences while allowing human agents to focus on conversations that require judgment.
By connecting conversational AI with account systems, payment workflows, and collection processes, organizations can provide customers with guided support while maintaining security controls and human oversight.
We help businesses design AI assistants that connect with their workflows, data requirements, and customer service goals.
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