A new arXiv paper proposes using synthetic customer agents as digital twins to validate LLM chatbots in regulated domains such as banking. The approach is meant to make testing more scalable before customer-facing systems go live.
The authors ground the synthetic agents in real transactional and conversational data, then condition them to represent different customer profiles and interaction styles. They report high semantic alignment with real customers, low hallucination rates, and controllable personality traits.
The validation framework combines LLM-as-a-judge evaluation, human expert testing, and adversarial probing. It tests scenario performance across emotional states, demographic groups, and linguistic factors rather than relying on a single average score.
The authors say the method was used to validate a customer-facing chatbot at a leading UK bank. The work does not remove the need for human review or regulatory oversight, but it offers a more systematic way to find failures before a chatbot reaches customers.