A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Luo, Jiaming Luo, Weiyi Sun, Guoqing Zhu, Mengchen Tang, Haifeng Lan, Kunyao Wu, Mengyue Zhu, Kenny Q. |
| author_facet | Luo, Jiaming Luo, Weiyi Sun, Guoqing Zhu, Mengchen Tang, Haifeng Lan, Kunyao Wu, Mengyue Zhu, Kenny Q. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge Luo, Jiaming Luo, Weiyi Sun, Guoqing Zhu, Mengchen Tang, Haifeng Lan, Kunyao Wu, Mengyue Zhu, Kenny Q. Information Retrieval Artificial Intelligence Computation and Language 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. |
| title | A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2504.06273 |