Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
This paper presents a two-part contribution for large-scale chatbot validation: a methodology for creating high-fidelity synthetic customer agents (SCAs) as digital twins, and an SCA-based validation framework combining automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing. The approach was validated at a leading UK bank, providing a scalable pathway toward regulatory compliance.
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[Submitted on 15 May 2026]
Title:Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
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Abstract:LLM-based chatbots are transforming customer service in regulated domains such as banking, but scalable and cost-effective validation remains a critical barrier to safe deployment. We present a two-part contribution for large-scale chatbot validation. First, we introduce a methodology for creating high-fidelity synthetic customer agents (SCAs) as digital twins, grounded in real transactional and conversational data, that enables automatic generation and behavioral conditioning to simulate diverse customer profiles and interaction styles. Evaluation demonstrates that SCAs achieve high semantic alignment with real customers, low hallucination rates, and successful personality trait reproduction with controllable interventions. Second, we develop an SCA-based validation framework combining automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing. Scenario-based validation across emotional states, demographic groups, and linguistic factors confirms robust performance. Our approach was used to validate a customer facing chatbot at a leading UK bank, providing financial institutions with a scalable pathway toward regulatory compliance.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.26060 [cs.CL]
(or arXiv:2607.26060v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.26060
arXiv-issued DOI via DataCite
Submission history
From: Cristovao Iglesias De Oliveira [view email] [v1] Fri, 15 May 2026 15:40:11 UTC (140 KB)
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