Boosting Financial Risk Prediction Model Using Attention Mechanism Based Recurrent Neural Networks with Red‐Tailed Hawks Algorithm

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The systemic prediction of financial risk issues has become a main attention in the area of finance. Financial risk is the main likelihood that stockholders will lose currency after they finance a business that has debt if the business flow of cash demonstrates insufficient to see its economic requirements. The incorporation of deep learning (DL) methods into financial risk forecast and investigation has altered conventional techniques. While traditional quantitative systems often trust basic metrics such as the highest reduction, the arrival of DL requires a more nuanced assessment, highlighting the model's generalization capability, particularly in market crises like stock market crashes. DL techniques are efficient in removing intricate patterns from massive data collections and become an effective model for forecasting financial trends. In this paper, we offer Boosting Financial Risk Prediction Model Using Attention Mechanism with Red‐Tailed Hawk (BFRPM-AMRTH) Algorithm. The presented BFRPM-AMRTH model aims to address the challenges of identifying and mitigating potential financial threats in a dynamic environment. Initially, the BFRPM-AMRTH technique applies the linear scaling normalization (LSN) data normalization technique to standardize the input features and ensure consistency across the dataset. In addition, the long short-term memory auto encoder with attention mechanism (LSTMA-AE) technique can be employed for classifying financial risks. Eventually, the red‐tailed hawk (RTH) algorithm adjusts the hyperparameter values of the LSTMA-AE algorithm optimally and outcomes in greater classification performance. To ensure the improved performance of BFRPM-AMRTH system, a huge range of simulation studies has been achieved and the obtained outcomes establish the advancement of the BFRPM-AMRTH system over the existing techniques