Applying a novel deep learning technique to credit hazard modeling in business financial management

In financial management, where precise credit default forecasting is crucial for well-informed decision-making, credit hazard modeling is an essential part of risk assessment. Predicting the possibility of a borrower failing on their financial commitments, such as loans or credit card payments, is the primary goal of credit hazard modeling. In this paper, we provide an innovative technique called Adaptive Gravitational Search Dynamic Convolutional NeuroNet (AGS-DCNN) for credit hazard modeling. The large-scale examination of consumers who have established accounts ultimately yields information relevant to the financial standing of medium-sized and small-sized businesses from various industries. Over the course of five years, we obtained 410 financial data in total from 80 distinct companies. The Z-Score preprocessing approach is used to normalize and transform financial data to make it more suitable for study. Principle Component Analysis (PCA) is a feature extraction approach used to minimize dimensionality and capture the most important characteristics, enhancing model efficiency and interpretability. AGSA is an innovative optimization approach that adapts to the scenario and optimizes the suggested architecture, guaranteeing that the network is well-tailored to the unique needs of credit hazard modeling. To estimate credit default events, the suggested AGS-DCNN approach is tested using real-world financial data. The results show that it outperforms existing credit risk models regarding the estimated recall, f1-score, accuracy, precision, and resilience. Furthermore, the AGS-DCNN approach is adaptable to varied datasets. It has the potential to understand complicated patterns in financial data, making it suited for a wide range of financial management applications.