# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 5 Conclusions  
This study addresses the research gap in the default risk of commercial bills among small and medium-sized enterprises (SMEs) by collecting multidimensional non-financial data and combining traditional machine learning techniques with advanced large language models. It offers new research insights for both academia and practical applications. The findings demonstrate the potential of large language models in financial risk prediction and provide an innovative analytical approach. Empirical analysis reveals that factors such as enterprise age, the number of legal disputes, the proportion of negative news, last year's default count, and the number of individuals subject to consumption restrictions are significantly correlated with the recent default count of commercial bills. However, enterprise age and the number of legal disputes did not exhibit robust significance in subsequent tests, highlighting the challenges of obtaining information about SMEs. Overall, researchers can acquire non-financial information about SMEs through public channels and select for the data that are most relevant to enterprise risk. In traditional machine learning methods, ensemble learning typically outperforms single classifiers. Specifically, XGBoost achieved the best results on the Z-score standardized dataset, with an accuracy and F1 score of 81.42% and 80%, respectively. In contrast, the random forest algorithm exhibited greater stability across various standardization methods, with average accuracy and F1 scores reaching 80.54% and 79.35%. Thus, when disregarding the data standardization \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline LLM & 75.00\% & 75.00\% & 76.50\% & 75.74\% \\ LLM-FT & 82.10\% & 77.00\% & 84.30\% & 80.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results of LLM and LLM-FT.  
approach, utilizing the random forest algorithm for default prediction is a more prudent choice. During the large language model phase, the fine-tuned model achieved an accuracy and F1 score of 82.1% and 84.3%, surpassing all traditional machine learning algorithms and showcasing the formidable reasoning capabilities of large language models. This study demonstrates feasibility but has limitations. Commercial bill default data are proprietary and only accessible through partnerships with financial institutions, which contrasts with the goal of using open, easily accessible data for SME risk research. Additionally, the correlation between the collected multi-dimensional data and corporate risk is relatively low. Certain non-financial indicators perform inconsistently across conditions, limiting the model's predictive accuracy to about 80%, which falls short of the results typically obtained with financial metrics. This study offers significant potential for expansion. Large language models could extend beyond sentiment analysis to deeper assessments of legal documents and court announcements, using advanced multimodal analysis for comprehensive sentiment insights. Enhancing current machine learning models through ensemble, deep learning, and reinforcement learning approaches--integrating temporal data--can refine risk predictions. Further, establishing an adaptive SME risk prediction platform with an up-to-date enterprise database could automate data retrieval, risk report generation, and leverage language models for interactive AI-driven user guidance and query resolution. In summary, this study not only offers a new perspective on risk management for SMEs but also lays a foundation for future research. Methodology, J.L. ; Formal analysis, H.H. ; Data curation, S.C.; Writing--original draft, H.H. ; Writing--review & editing, C.Z. ; Supervision, X.C. ; Investigation, X.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant 62302400, 62376227, and the Fundamental Research Funds for the Central Universities.