LLM-infused bi-level semantic enhancement for corporate credit risk prediction

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sichong Lu , Yi Su , Xiaoming Zhang , Jiahui Chai , Lean Yu
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引用次数: 0

Abstract

Corporate credit risk (CCR) prediction enables investors, governments, and companies to make informed financial decisions. Existing research primarily focuses solely on the tabular feature values, yet it often overlooks the rich inherent semantic information. In this paper, a novel bi-level semantic enhancement framework for CCR prediction is proposed. Firstly, at the data-level, a large language model (LLM) generates detailed textual descriptions of companies’ financial conditions, infusing raw tabular training data with semantic information and domain knowledge. Secondly, to enable semantic perception during inference when only tabular data is available, a contrastive multimodal multitask learning model (CMML) is proposed at the model level. CMML leverages the semantically enhanced data from the previous level to acquire semantic perception capabilities during the training phase, requiring only tabular data during prediction. It aligns the representations of tabular data with textual data, enabling extracting semantically rich features from tabular data. Furthermore, a semantic alignment classifier and an MLP classifier are integrated into a weighted ensemble learner within a multitask learning architecture to enhance robustness. Empirical verification on two datasets demonstrates that CMML surpasses benchmark models in key metrics, particularly in scenarios with limited samples and high proportions of unseen corporations, implying its effectiveness in CCR prediction through bi-level semantic enhancement.
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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