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            <namePart type="given">Chung-Chi</namePart>
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    <abstract>Facilitating financial technology with the large-language model (LLM) has been developing in recent years. To address the challenges in one of the biggest world-wide markets, China, Chinese-expertise financial LLM has also been studied. The related works focus on conventional NLP tasks in finance, while developing LLM for specific tasks is also required. Besides, in the credit loan business, the existing AI-based approaches are largely related to Credit like Credit rating and Fraud prediction, while credit product customization is still missing. In China, Inclusive Finance and Rural Finance become two hot topics that raise critical challenges in flexibly customizing credit products to meet the variable fund requirements of small &amp; micro businesses, individual businesses, and agricultural businesses of local character. In this paper, the credit product customization is studied by developing an LLM-based financial AI assistant for the credit loan business. It is proposed to satisfy the business requirements of customer counseling, recommendation, and question-answers regarding credit loans. The proposed LLM is developed by Chinese prompt data automatically constructed based on a small set of real-world credit products. The experiments demonstrate its effectiveness in credit loan-related ability while maintaining comparable performance in conventional finance NLP tasks.</abstract>
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%0 Conference Proceedings
%T CreditLLM: Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data
%A Yan, Sixing
%A Zhu, Ting
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yan-zhu-2025-creditllm
%X Facilitating financial technology with the large-language model (LLM) has been developing in recent years. To address the challenges in one of the biggest world-wide markets, China, Chinese-expertise financial LLM has also been studied. The related works focus on conventional NLP tasks in finance, while developing LLM for specific tasks is also required. Besides, in the credit loan business, the existing AI-based approaches are largely related to Credit like Credit rating and Fraud prediction, while credit product customization is still missing. In China, Inclusive Finance and Rural Finance become two hot topics that raise critical challenges in flexibly customizing credit products to meet the variable fund requirements of small & micro businesses, individual businesses, and agricultural businesses of local character. In this paper, the credit product customization is studied by developing an LLM-based financial AI assistant for the credit loan business. It is proposed to satisfy the business requirements of customer counseling, recommendation, and question-answers regarding credit loans. The proposed LLM is developed by Chinese prompt data automatically constructed based on a small set of real-world credit products. The experiments demonstrate its effectiveness in credit loan-related ability while maintaining comparable performance in conventional finance NLP tasks.
%U https://aclanthology.org/2025.finnlp-1.13/
%P 141-152

Markdown (Informal)

[CreditLLM: Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data](https://aclanthology.org/2025.finnlp-1.13/) (Yan & Zhu, FinNLP 2025)

• CreditLLM: Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data (Yan & Zhu, FinNLP 2025)

ACL

• Sixing Yan and Ting Zhu. 2025. CreditLLM: Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 141–152, Abu Dhabi, UAE. Association for Computational Linguistics.