Bankruptcy forecasting in enterprises and its security using hybrid deep learning models - Akshat Gaurav; Brij B. Gupta; Shavi Bansal; Konstantinos E. Psannis (Cyber Security and Applications)
In current scenario when economic and risk management sectors need accurate predictions of enterprise bankruptcy, it is very importance issue to research in the field of security of enterprise bankruptcy. In this context, we propose an hybrid deep learning model through the use of convolutional neural network to enhance bankruptcy forecasting models. We address the high-dimensional data and imbalanced problems by introducing feature selection strategically and Synthetic Minority Over-sampling Technique (SMOTE). In a comparative evaluation, the performance of our model is over 81 %, which is better than that for Logistic Regression and Support Vector Machines. This leap in accuracy demonstrates the cutting edge unprecedented ability of our model to decrypt complex financial patterns and establishes a new precedent for deep learning applications in the nuanced field of financial analytics.