A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge
Jiaming Luo, Weiyi Luo, Guoqing Sun, Mengchen Zhu, Haifeng Tang, Kenny Q. Zhu, Mengyue Wu
Abstract
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.
BibTeX
@inproceedings{luo-etal-2025-diverse,
title = "A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge",
author = "Luo, Jiaming and
Luo, Weiyi and
Sun, Guoqing and
Zhu, Mengchen and
Tang, Haifeng and
Zhu, Kenny Q. and
Wu, Mengyue",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.11/",
pages = "129--137",
ISBN = "979-8-89176-194-0"
}