Digital Twin Simulations Validate Chatbots at Scale.

Validating LLM-based chatbots for customer service, especially in regulated sectors like banking, presents significant challenges in terms of scalability and cost. This paper introduces a novel two-part solution to address these issues. First, it details a methodology for creating high-fidelity synthetic customer agents (SCAs), essentially digital twins of real customers. These SCAs are grounded in actual transactional and conversational data, allowing for automatic generation and behavioral conditioning to simulate diverse customer profiles and interaction styles. Evaluations confirm SCAs achieve high semantic alignment with real customers, low hallucination rates, and controllable personality trait reproduction. Second, the research develops an SCA-based validation framework that combines automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing. This framework enables scenario-based validation across various emotional states, demographic groups, and linguistic factors, confirming robust chatbot performance. This approach has been successfully deployed by a major UK bank, providing a scalable and cost-effective pathway for financial institutions to achieve regulatory compliance and safe chatbot deployment.