Towards Effective Automatic Debt Collection with Persona Awareness
Tong Zhang, Junhong Liu, Chen Huang, Jia Liu, Hongru Liang, Zujie Wen, Wenqiang Lei
Abstract
Understanding debtor personas is crucial for collectors to empathize with debtors and develop more effective collection strategies. In this paper, we take the first step towards comprehensively investigating the significance of debtor personas and present a successful commercial practice on automatic debt collection agents. Specifically, we organize the debtor personas into a taxonomy and construct a persona-aware conversation dataset. Building upon it, we implement a simple yet effective persona-aware agent called PAD. After two-month online testing, PAD increases the recovery rate by 3.31% and collects an additional ~100K RMB. Our commercial practice brings inspiration to the debt collection industry by providing an effective automatic solution.- Anthology ID:
- 2023.emnlp-industry.4
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–45
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.4
- DOI:
- 10.18653/v1/2023.emnlp-industry.4
- Cite (ACL):
- Tong Zhang, Junhong Liu, Chen Huang, Jia Liu, Hongru Liang, Zujie Wen, and Wenqiang Lei. 2023. Towards Effective Automatic Debt Collection with Persona Awareness. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 32–45, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Towards Effective Automatic Debt Collection with Persona Awareness (Zhang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-industry.4.pdf