Credit Risk Forecasting in FinTech Using Hybrid Ensemble Models: A Comparative Performance Study

The credit risk forecasting is one of the pillars of financial stability and especially in the fast-growing FinTech industry where the data diversity and magnitude have made old models less and less sufficient.The performance of hybrid ensemble models, i.e., systems that combine machine learning and deep learning algorithms, is reviewed and compared to predict credit risk in FinTech environments in this paper.Conventional statistical tools like logistic regression are highly interpretable but unable to address multiple high-dimensional and unstructured data.Random Forest and Gradient Boosting are machine learning models that optimize predictive accuracy but do not provide transparency.Hybrid ensemble models, in contrast, combine the best abilities of different models to enhance their accuracy and strength.The review indicates that a hybrid ensemble performs better than an individual learner in the characteristics like AUC, precision, and F1-score at the same time of being flexible to the large scale and real time FinTech applications.Interpretability, computational scalability and ethical fairness are among the major challenges.The paper presents a conclusion that the future needs of research focusing explainable and privacy preserving and fair hybrid ensemble systems should be considered so that transparent and responsible credit risk management in digital finance can be established.