C redit LLM : Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data (2025)

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 & 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.

## introduction with the development of large-language model (llm) technology, how to use llm to empower specific businesses in vertical domain has become a research topic. in the financial domain, inspired by the excellent reading&comprehension ability and open-domain question answering ability of llms, using llm in the financial domain to empower financial businesses and improve work efficiency has received increasing attention. developing llm in the financial domain involves using pre-trained general-domain models as the foundation model, conducting continuous pre-training on finance-domain data, and supervised training on various downstream task datasets related to finance business. these downstream tasks generally include text classification @xcite , sentiment analysis @xcite , entity recognition @xcite @xcite , event detection @xcite , document summarization @xcite and report generation @xcite in the specific implementation of financial business. generally speaking, large-scale generaldomain models have basic abilities in solving common finance-related nlp tasks because of their high model capacity and complexity. however, for complex financial business requiring high accuracy, it is necessary to develop llm specialized for the financial domain. early financial llm is developed under the english context, using english financial data to fine-tune the general-domain foundation model, such as bloomberggpt @xcite , fin-gpt @xcite @xcite , and pixiu @xcite . in the chinese context, some research work is dedicated to develop financial llm based on chinese financial data, such as bbt-fin @xcite , xuanyuan @xcite , cornucopia (yu, 2023), disc finllm @xcite , and cfgpt @xcite . these llms have achieved excellent performance in various tasks in the financial domain, demonstrating the feasibility of empowering financial companies in the chinese context. in terms of specific financial business, there are few llm works studied while they are usually used as certain applications developed by internal data. this paper focuses on llm in credit loan business in the chinese context. from the perspective of machine learning, the research of credit business can generally be divided into two aspects: "credit estimation" and "loan product design". "credit estimation" estimates customers' loan repayment ability, including credit rating prediction @xcite , default prediction @xcite , and fraud identification @xcite ; while "loan product design" focuses on the actual needs of customers, including product recommendation, product-customer matching, and credit products personalization. currently, china's finance is actively developing "inclusive finance" and "rural finance" to empower the physical industry with finance and benefit the whole society. on this basis, for diversified and non-standard customer needs (such as "funding needs for local characteristic agricultural products"), credit business is more reflected in personalized customization of credit products. unlike existing works, this paper focuses on using llm to diversified personalized credit business customization. by a few data of credit products, the instruction-following data of credit business is automatically constructed, covering four kinds of downstream tasks: credit product counseling, product-customer matching, credit product recommendation and credit knowledge q&a. meanwhile, we uses the foundation llm pretrained on chinese finance-domain as the base model to further develop a finance ai assistant for credit business, named creditllm. ## literature review nowadays, with the success of pre-trained language models (plm), expanding the capabilities of plm by large scale setting has become a new research hotspot. the generative pre-trained transformer (gpt) has been publicly released and its excellent reading comprehension and question answering interaction capabilities further mark the llm as a new milestone in language model research. today, existing llms (openai, 2021b,a) demonstrate their exceptional natural language understanding (nlu) capabilities guided by instructions without further training. the outstanding ability of llms in nlg has also attracted the attention of research in the financial domain. as a pioneering work of llm in the financial domain, bloomberg, the world's largest financial information company, proposed the first financial llm bloomberggpt @xcite , which is trained based on various financial data to develop universal nlp ability in the financial domain. subsequently, investlm @xcite , which focused on investment-related capabilities, was proposed to explore the fully fine-tuning approach in the construction of financial llm. by fine-tuning llm with instructionfollowing data, its ability in domain transfer learning has been validated. for instance, fingpt @xcite @xcite and pixue @xcite worked on constructing financial llm based on instructionfollowing data. in the chinese context, research of the chinese financial llm has also received attention. in the q&a scenario, xuanyuan @xcite achieves high-level model performance in the general and financial fields, while cornucopia (yu, 2023) further improves causal inference related to finance. cfgpt @xcite uses the large volume of chinese financial data to further train on the basis of interlm @xcite , enhancing the ability of financial llms in various tasks related to the financial domain. ## conclusion a framework for developing credit business capabilities in the chinese financial domain using a large-language model is proposed in this paper. the framework constructs a customer communication categorization of credit loan business and the method of credit product structuration. then, with a few real-world credit products, the instruction fine-tuning data is automatically constructed for developing the finance ai assistant for credit loan business. however, the catastrophic forgetting of the original capability is still observed. the possible directions for future research work could include constructing large-scale instructionfollowing data of credit loan products by chatbased llm models and artificial templates, alleviating catastrophic forgetting, and editing llm memory of irrelevant content.