Credit C-GPT: A Domain-Specialized Large Language Model for Conversational Understanding in Vietnamese Debt Collection

TL;DR

Credit C-GPT is a large, domain-specific Vietnamese language model designed for conversational understanding in debt collection, integrating multiple tasks to improve real-time contact center assistance and analytics.

Contribution

This paper presents Credit C-GPT, a 7-billion-parameter model fine-tuned for Vietnamese debt collection conversations, combining multiple conversational tasks in a single framework.

Findings

Outperforms traditional pipeline approaches in accuracy

Enhances real-time contact center support

Provides scalable, privacy-aware analytics

Abstract

Debt collection is a critical function within the banking, financial services, and insurance (BFSI) sector, relying heavily on large-scale human-to-human conversational interactions conducted primarily in Vietnamese contact centers. These conversations involve informal spoken language, emotional variability, and complex domain-specific reasoning, which pose significant challenges for traditional natural language processing systems. This paper introduces Credit C-GPT, a domain-specialized large language model with seven billion parameters, fine-tuned for conversational understanding in Vietnamese debt collection scenarios. The proposed model integrates multiple conversational intelligence tasks, including dialogue understanding, sentiment recognition, intent detection, call stage classification, and structured slot-value extraction, within a single reasoning-based framework. We describe the data construction process, annotation strategy, and training methodology, and evaluate the model on proprietary human-annotated datasets. Experimental results show consistent improvements over traditional pipeline-based approaches, indicating that domain-specialized conversational language models provide a scalable and privacy-aware solution for real-time assistance and post-call analytics in enterprise contact centers.