Proposed evaluation framework enhances chatbot performance measurement in banking and insurance sectors, suggesting standardized practices for better customer engagement.
The integration of artificial intelligence (AI) technologies, particularly chatbots, has transformed customer service operations across various sectors, with banking and insurance standing out due to their high dependence on data accuracy, regulatory compliance, and customer engagement. In these sectors, chatbots are not merely add-ons but integral tools for streamlining service delivery, enhancing user satisfaction, reducing operational costs, and maintaining 24/7 availability. Despite their increasing adoption, the effectiveness of chatbots in these industries is frequently evaluated using fragmented and sector-specific methodologies, leading to inconsistencies in performance assessment and implementation strategies. The absence of a unified evaluation framework undermines the ability of stakeholders to compare chatbot deployments, adopt best practices, and optimize performance in line with strategic business goals. This paper proposes a comprehensive, unified evaluation framework designed specifically for banking and insurance chatbot applications. It incorporates both general performance indicators as response accuracy, response time, natural language processing quality, and user satisfaction industry-specific metrics, including transaction success rate, fraud detection efficiency, regulatory compliance adherence, policy recommendation accuracy, and claims processing efficiency. The framework was developed through a systematic literature review, comparative industry analysis, and synthesis of performance criteria identified in academic and professional research. Although conceptual at this stage, it offers a scalable and adaptable model suitable for real-world applications. Future research will be necessary to empirically validate this model and refine it through iterative field testing and expert feedback. By offering a standardized, multidimensional tool for evaluating chatbot performance, this paper contributes to the broader discourse on AI deployment in critical service industries and supports financial institutions in harnessing the full potential of chatbot technology.
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Howard et al. (2025) studied this question.
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