Against the backdrop of the rapid rise of the information technology industry and the era of big data, enterprises are facing increasingly complex and diversified financial challenges. These challenges cover the acceleration of technological updates, the increase in financial pressure, as well as the increasingly fierce market competition and uncertainty in financial markets. These factors collectively drive a deeper and more refined demand for financial risk management. In this context, building a model that can accurately predict financial risks has become crucial. By integrating complex network theory and lightGBM algorithm, it is possible to gain a more comprehensive insight into the interrelationships and risk factors between enterprises, thereby improving the accuracy and timeliness of financial risk prediction. This model not only helps enterprises detect potential financial risks in a timely manner, but also provides effective warning and response strategies, thereby enhancing the risk control ability and operational stability of the enterprise. This study is of great significance for risk management in information technology enterprises and the entire industry, and also provides useful ideas and methods for further optimizing future financial risk prediction models.
Read full abstract7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access