Big Data and Related Model Algorithms in Commercial Bank Credit Evaluation

Abstract

Contemporarily, with the development of big data and related machine learning models and algorithms, commercial banks no longer possess advantages in traditional risk control and need to introduce big data and its algorithmic models as a new means of risk evaluation. The introduction of big data and its model algorithms by some commercial banks and fintech companies has proven its outstanding effect in the field of risk control. Based on the evaluations of XGBoost model, OneClassSVM model and other models, the article analyses the application of related technologies in the fields of default prediction and public opinion identification. Besides, this study combines the analysis with the scenarios in which commercial banks can implement big data and model algorithms for risk evaluation. In this case, it provides a new feasible method for commercial banks' risk evaluation and control, and proves the possibility of the application of related technologies with great effect. These results play an important role in promoting the application of big data and its models for risk evaluation and control in commercial banks.