Towards novel financial risk prediction method with machine learning model

Among the multiple risks faced by enterprises, financial risk is particularly prominent in the big data environment. Serious data imbalance has become a major challenge in the analysis of corporate financial risks. Aiming at the sample imbalance problem in enterprise competitive intelligence analysis, this paper proposes an enterprise risk identification method oriented to unbalanced samples, taking credit risk prediction of financial enterprises as a starting point. The method utilizes intelligent analysis means such as feature selection, unbalanced sample balance processing and integrated learning in the field of artificial intelligence, aiming to provide a solution to the problem of enterprise risk identification in enterprise competitive intelligence under the big data environment.