Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent

Debt collection negotiations (DCN) are vital for managing non-performing loans (NPLs) and reducing creditor losses.Traditional methods are labor-intensive, while large language models (LLMs) offer promising automation potential.However, prior systems lacked dynamic negotiation and real-time decisionmaking capabilities.This paper explores LLMs in automating DCN and proposes a novel evaluation framework with 13 metrics across 4 aspects.Our experiments reveal that LLMs tend to over-concede compared to human negotiators.To address this, we propose the Multi-Agent Debt Negotiation (MADeN) framework, incorporating planning and judging modules to improve decision rationality.We also apply post-training techniques, including DPO with rejection sampling, to optimize performance.Our studies provide valuable insights for practitioners and researchers seeking to enhance efficiency and outcomes in this domain.