基于真实对话数据构建多样化催收脚本系统，提升响应相关性与可部署性。
A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge
- 采用两阶段检索机制保证回复上下文相关性
- 通过知识蒸馏实现脚本多样性与实际部署效率提升
- 适用于金融行业自动化催收场景
设计高效的债务催收系统对提升金融行业运营效率和降低成本至关重要。然而，保持话术多样性、上下文相关性和连贯性带来了显著挑战。本文基于某大型商业银行的真实催收对话数据，构建了话术库，并提出一种两阶段检索式应答系统以增强上下文相关性。实验结果表明，该系统提升了话术多样性，增强了回复相关性，并通过知识蒸馏实现了实际部署效率的优化。该工作提供了一种可扩展的自动化解决方案，为推动真实应用场景中的债务催收实践提供了重要参考。
原文摘要 · Abstract (English)
Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.
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