A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
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| _version_ | 1866918407136346112 |
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| author | Du, Huaming Feng, Cancan Lei, Yuqian Zhang, Chenyang Liu, Guisong Kou, Gang Yang, Carl Zhao, Yu |
| author_facet | Du, Huaming Feng, Cancan Lei, Yuqian Zhang, Chenyang Liu, Guisong Kou, Gang Yang, Carl Zhao, Yu |
| contents | Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has always been the core research topic in the fields of Finance and Management. Based on advanced computer science and artificial intelligence technologies, enterprise risk analysis research is experiencing rapid developments and making significant progress. Therefore, it is both necessary and challenging to comprehensively review the relevant studies. Although there are already some valuable and impressive surveys on enterprise risk analysis from the perspective of Finance and Management, these surveys introduce approaches in a relatively isolated way and lack recent advances in enterprise financial risk analysis. In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from the perspective of Big Data and large language models. Specifically, this survey connects and systematizes existing research on enterprise financial risk, offering a holistic synthesis of research methods and key insights. We first introduce the problem formulation of enterprise financial risk in terms of risk types, granularity, intelligence levels, and evaluation metrics, and summarize representative studies accordingly. We then compare the analytical methods used to model enterprise financial risk and highlight the most influential research contributions. Finally, we identify the limitations of current research and propose five promising directions for future investigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_14997 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective Du, Huaming Feng, Cancan Lei, Yuqian Zhang, Chenyang Liu, Guisong Kou, Gang Yang, Carl Zhao, Yu Risk Management Artificial Intelligence Machine Learning Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has always been the core research topic in the fields of Finance and Management. Based on advanced computer science and artificial intelligence technologies, enterprise risk analysis research is experiencing rapid developments and making significant progress. Therefore, it is both necessary and challenging to comprehensively review the relevant studies. Although there are already some valuable and impressive surveys on enterprise risk analysis from the perspective of Finance and Management, these surveys introduce approaches in a relatively isolated way and lack recent advances in enterprise financial risk analysis. In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from the perspective of Big Data and large language models. Specifically, this survey connects and systematizes existing research on enterprise financial risk, offering a holistic synthesis of research methods and key insights. We first introduce the problem formulation of enterprise financial risk in terms of risk types, granularity, intelligence levels, and evaluation metrics, and summarize representative studies accordingly. We then compare the analytical methods used to model enterprise financial risk and highlight the most influential research contributions. Finally, we identify the limitations of current research and propose five promising directions for future investigation. |
| title | A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective |
| topic | Risk Management Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2211.14997 |