用AI助手对话探查，精准分流银行客户求助
Helping Customers in Distress: An LLM-powered Agent that Converses, Probes, and Routes
- 基于大模型的多轮对话探查，自动分类客户问题
- 提升分类准确率30.6%，显著优于人工流程
- 适合需要高效客服分诊的金融机构使用
银行每年收到数百万起欺诈、诈骗及交易争议报告，现有由人工主导的处理流程耗时且压力大。为此，我们开发了一个面向客户的AI辅助分诊代理，利用大语言模型（LLMs）开展多轮对话，主动提问并根据政策规则对案件进行分类，实现嵌入式客户服务。为评估和持续优化该系统，我们基于历史数据构建了真实客户的数字孪生体，生成了涵盖多种现实场景的带标签对话数据。实验结果表明，该代理在历史案例分诊中准确率提升了30.6%，专家满意度高，验证了针对性探查在规模化银行运营中的有效性。
原文摘要 · Abstract (English)
Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the appropriate specialist teams for assistance. The existing manual process driven by humans is slow and stressful for both customers and staff. To address this, we develop a customer-facing AI powered triaging agent that leverages large language models (LLMs) to conduct multi-turn conversations, ask relevant questions, and classify cases for accurate, policy-guided routing, making it embedded in the customer journey. To evaluate and continuously improve the agent, synthetic digital twins of real customers were simulated, generating realistic, labelled dialogues based on historical data to test a wide range of real-world scenarios. This work details the triage agent's modelling approach, integration with policy, safety guardrails and reasoning frameworks, the use of the synthetic agent for scalable evaluation, and findings on the AI system's accuracy, robustness, and compliance. Results show that the agent successfully improves triaging of historical cases, achieving a 30.6% increase in classification accuracy, with high satisfaction levels reported by our subject-matter experts, highlighting how targeted probing can lead to more effective triage in banking operations at scale.
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