New method validates banking chatbots using synthetic customer agents

Researchers have developed a novel methodology for validating large-scale chatbots, particularly for regulated industries like banking. This approach involves creating high-fidelity synthetic customer agents (SCAs) that act as digital twins, capable of simulating diverse customer profiles and interaction styles based on real data. The framework combines automated LLM-as-a-Judge evaluations with human expert testing and adversarial probing to ensure robust performance across various conditions. This method has been successfully applied to validate a customer-facing chatbot at a UK bank, offering a scalable path for financial institutions to meet regulatory compliance. AI

IMPACT Provides a scalable pathway for financial institutions to achieve regulatory compliance for their chatbots.

RANK_REASON The item is a research paper detailing a new methodology for chatbot validation. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• Cristovao Iglesias De Oliveira
• Customer Digital Twin Simulations
• DagsHub
• Gotit.pub
• Hugging Face
• LLM-as-a-Judge
• ScienceCast
• Synthetic Customer Agents
• UK

• paper
• product
• safety

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