A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
In the rapidly evolving domain of Finance and Management, enterprise financial risk analysis has emerged as a critical area of research. The ability to predict future financial risks is essential for enterprises seeking to navigate uncertain economic landscapes. Recent advancements in computer science and artificial intelligence have catalyzed significant progress in this field. A comprehensive review of existing studies is not only necessary but also poses unique challenges due to the vast array of methodologies and insights available.
A recent paper, identified as arXiv:2211.14997v5, seeks to fill the gap left by previous surveys by systematically reviewing enterprise financial risk analysis through the lenses of Big Data and large language models (LLMs). The authors argue that while prior surveys have contributed valuable insights, they often present approaches in isolation and fail to incorporate the latest advancements in the field.
Key Contributions of the Survey
This literature survey is noteworthy for several reasons:
- Problem Formulation: The survey begins by defining the problem of enterprise financial risk. This includes an examination of various risk types, granularity levels, intelligence levels, and evaluation metrics, which are crucial for understanding the nuances of financial risk.
- Systematic Review: It connects and organizes existing research on enterprise financial risk, offering a holistic synthesis of various research methods and critical insights. By doing so, it provides a structured approach to understanding the complexity of financial risk analysis.
- Analytical Comparisons: The paper compares analytical methods employed in modeling enterprise financial risk. This comparative analysis highlights the most influential contributions in the field, allowing researchers to identify best practices and innovative methodologies.
- Identification of Limitations: The authors do not shy away from addressing the limitations of current research. By doing so, they pave the way for future research initiatives that could fill existing gaps and enhance the understanding of financial risk.
- Future Directions: The survey proposes five promising directions for future investigation. These directions aim to inspire subsequent research efforts and explore uncharted territories in enterprise financial risk analysis.
Conclusion
The survey on enterprise financial risk analysis from the perspective of Big Data and LLMs represents a significant step forward in the field. By systematically reviewing existing literature and providing a comprehensive synthesis of research methods, it serves as a valuable resource for researchers and practitioners alike. As enterprises continue to face increasing financial uncertainties, the insights derived from this survey will be instrumental in guiding future research and enhancing the predictive capabilities of financial risk analysis.