Strategic Integration Of Business Intelligence And Risk Management For Financial Institutions Using A Deep Random Forest Approach

In the dynamic field of finance, it is crucial to integrate risk management and business intelligence (BI and RM, respectively) in a strategic manner to achieve long-term success. Because of the growing data and risk complexity, financial organisations need to take a more nuanced approach to the integration of business intelligence and risk management. When utilising traditional methodologies, it is difficult to capture the intricate patterns and linkages that exist within massive datasets. This difficulty can result in erroneous risk assessments and poor decision making. Deep Random Forests have the ability to bridge the gap between business intelligence and risk management; yet, there is a paucity of research being conducted into its application in this context at the present time. This study aims to address that hole by proposing and proving a novel framework that makes use of deep learning to improve the process of making financial decisions. Through the application of advanced approaches, most notably the use of a Deep Random Forest Approach, this body of work addresses the urgent necessity of improving decision-making procedures. In this investigation, we use an approach called the Deep Random Forest Approach to combine the advantages of ensemble approaches with the benefits of deep learning. Because the model is trained using a considerable amount of historical data, it is able to recognise nuanced patterns and relationships that are often missed by conventional approaches to business intelligence and risk management. Deep learning has been incorporated into the model, which has resulted in an improvement in the model ability to handle the intricate and nonlinear interactions that are typical of financial datasets. The results demonstrate that the Deep Random Forest Approach is superior to approaches that are more commonly used. The improved prediction accuracy and resilience of the model demonstrates its potential to revolutionise the decision support systems of financial institutions. The findings pave the way for a business intelligence and risk management strategy that is both more robust and more informed.