AI Digital Twins of Customers Validate Banking Chatbots at Scale

A new paper from researchers including Cristovao Iglesias, Devesh Batra, and others introduces a large-scale validation methodology for LLM-based chatbots using customer digital twin simulations. The key innovation is the creation of high-fidelity synthetic customer agents (SCAs) that are grounded in real customer transactional and conversational data from a leading UK bank. These SCAs automatically generate diverse customer profiles, emotional states, and interaction styles, while reproducing personality traits and achieving high semantic alignment with real customers. The SCAs exhibit low hallucination rates and allow controllable interventions for targeted testing.

The validation framework combines three complementary approaches: automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing. Scenario-based testing across emotional states, demographic groups, and linguistic factors confirmed robust chatbot performance. This methodology was successfully used to validate a customer-facing chatbot at a major UK bank, providing a scalable pathway toward regulatory compliance. For financial institutions and other regulated industries, this approach significantly reduces the time and cost of safe chatbot deployment while improving coverage of edge cases and sensitive interactions.