A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge
This provides a scalable automated solution for the financial industry to enhance debt collection practices, though it appears incremental as it builds on existing retrieval methods.
The paper tackles the challenge of designing effective debt collection systems by proposing a two-stage retrieval-based system using real debtor-collector data, which improves script diversity and response relevance while achieving practical deployment efficiency through knowledge distillation.
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.