# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
###### Abstract  
Predicting default risk in commercial bills for small and medium-sized enterprises (SMEs) is crucial, as these enterprises represent one of the largest components of a nation's economic structure, and their financial stability can impact systemic financial risk. However, data on the commercial bills of SMEs are scarce and challenging to gather, which has impeded research on risk prediction for these businesses. This study aims to address this gap by leveraging 38 multi-dimensional, non-financial features collected from 1972 real SMEs in China to predict bill default risk. We identified the most influential factors among these 38 features and introduced a novel prompt-based learning framework using large language models for risk assessment, benchmarking against seven mainstream machine learning algorithms. In the experiments, the XGBoost algorithm achieved the best performance on the Z-Score standardized dataset, with an accuracy of 81.42% and an F1 score of 80%. Additionally, we tested both the standard and fine-tuned versions of the large language model, which yielded accuracies of 75% and 82.1%, respectively. These results indicate that the proposed framework has significant potential for predicting risks in SMEs and offers new insights for related research. large language model; commercial bill default; financial credit risks; SMEs +
Footnote †: journal: Applied sciences  
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significantly outperform logistic regression models in terms of the resolution and reliability of predicting default probabilities under conditions of small training samples and high input data variance. [PERSON] (2020) [5] also found that artificial neural networks are more effective in predicting credit risk than the Naive Bayes (NB) method. [PERSON] (2022) [6] integrated SVM and BP neural network algorithms to construct models that enhance the accuracy of credit risk prediction. However, these studies mainly rely on the financial data of listed companies and are effective only for specific stages or types of data, lacking flexibility and timeliness, and are difficult to adapt to changes in policies and the international environment. To address these limitations, this study introduces a novel approach. First, we collect multi-dimensional non-financial data from SMEs, including business registration information, legal documents, news sentiment, and personal information on high-consumption restrictions. Second, leveraging the prompt learning capabilities of large models, we enhance model flexibility and timeliness without retraining by utilizing the general capabilities of large models through prompt learning. This method not only adapts to the evolving market environment but also enables default risk prediction at different stages, providing financial institutions and decision-makers with more accurate and real-time risk assessment tools. The innovative contributions of this study can be enumerated as follows:  
* Innovating an indicator system: This study engages with company bill default data and emphasizes non-financial data, such as registration information, legal documentation, and sentiment assessed in news articles. This approach helps us to formulate a new barometer for risk assessment. * Evaluating the effectiveness of different data standardization techniques: This study explores the impact of three unique data standardization methods on the experimental outcomes, offering new insights for enterprise risk analysis. Identifying the most suitable standardization technique to optimize the model represents an innovative addition to the research. * Incorporating large language models: Our cutting-edge combination of large language model prompts into company credit risk assessment offers a novel research perspective. This serves as an innovative approach towards corporate risk prediction.