Financial Risk Prediction Using Multiple Machine Learning And Deep Learning Techniques

Accurately predicting financial risks such as corporate bankruptcy is crucial for safeguarding investments and ensuring economic stability. With the increasing complexity and volume of financial data, traditional analytical techniques often fall short in handling non-linear patterns and imbalanced datasets. This paper presents a comprehensive framework for financial risk prediction using machine learning and deep learning techniques. We evaluate the effectiveness of several base classifiers, ensembles tacking, and a novel deep hybrid model that combines predictions from multiple learners into a neural network meta-classifier. Experimental results o n a real-world bankruptcy dataset show that the deep hybrid ensemble outperforms all other models, achieving a classification accuracy of 98.98% and an F1-score of 98.98%. These findings highlight the potential of layered learning architectures for reliable and interpretable financial analysis and forecasting.