USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT - Joseph Olorunfemi AKANDE (International Journal of Social and Educational Innovation)
In this study, we explore the current applications of advanced technologies in financial risk management, specifically focusing on various approaches to managing financial risks. We conducted a comprehensive review of existing literature and identified areas where these technologies have been extensively researched, as well as topics that require further investigation. Well-researched fields include credit rating, fraud detection, bankruptcy prediction, and volatility forecasting. In these areas, advanced models, including deep learning, have been widely employed to improve accuracy and predictive power. However, certain sectors such as claims modeling, loss reserving, and mortality forecasting have not been given the same level of attention. Our analysis highlights the extensive use of sophisticated statistical models in financial risk management. While some progress has been made, more challenges need to be addressed, especially in traditional statistical models. Recent advancements in machine learning, particularly deep learning, offer significant potential for improving the efficiency and effectiveness of risk management systems. These advancements provide more accurate models for dealing with complex data, making them highly valuable for the financial performance management (FPM) field. One notable area of recent development is the incorporation of uncertainty estimation techniques in machine learning models, which allow for more precise risk assessment in unpredictable financial environments. Moreover, federated learning systems present a promising solution for ensuring privacy and security when dealing with sensitive financial data. This approach allows for collaborative learning without compromising the confidentiality of data, an essential factor in financial risk management. Despite these advances, there is still much work to be done regarding the explainability and fairness of machine learning models used in financial risk management. Ensuring that these models are transparent and unbiased is crucial for their broader adoption in the financial industry. Future research must prioritize developing models that can provide clear explanations of their decision-making processes, as well as ensuring that they do not disproportionately affect certain groups. In conclusion, our review of current applications in financial risk management highlights both well-explored areas and emerging opportunities for future research. While deep learning models have significantly improved predictive capabilities in some fields, other areas, such as claims modeling and loss reserving, require more focused research. The financial industry stands to benefit greatly from recent advancements in machine learning, but challenges around model transparency, fairness, and data security must be addressed to realize their full potential.