Extracting narrative data via large language models for loan default prediction: when talk isn’t cheap

Yufei Xia, Zhengxu Shi, Xiaoying Du and Qiong Zheng

Applied Economics Letters, 2025, vol. 32, issue 4, 481-486

Abstract: We aim to examine how the Large Language Model (LLM) can contribute to loan default prediction by extracting narrative data. Based on a Chinese FinTech lending platform dataset, we employ four LLMs to predict the probability of default (PD-LLM) based on the narrative data and use the PD-LLM as an additional feature to predict default loans. The empirical results show that the narrative data contain some extra credit information and can hardly be regarded as ‘cheap talks’. The extracted information via LLMs processes some predictive capability to predict default loan applications in both in- and out-of-sample analysis. The out-of-sample results indicate that including PD-LLM can significantly improve out-of-sample forecasting performance. At the same time, the rule-based linguistic characteristics and Word-Frequency-based Models hinder out-of-sample forecasting.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2023.2275647 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:32:y:2025:i:4:p:481-486

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2023.2275647

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().