AN EFFECTIVE APPROACH TO MANAGING FINANCIAL RISKS USING MACHINE LEARNING TECHNIQUES

Abstract

The research titled "An Effective Approach to Managing Financial Risks Using Machine Learning Techniques" explores the use of machine learning techniques in financial risk management, specifically in modelling financial volatility. The study uses a dataset of 30,000 rows and 25 columns containing a mix of numeric and categorical variables. The paper compares the performance of different machine-learning models and classical volatility models and finds that machine-learning models outperform classical models in terms of accuracy and predictive power. The research concludes that machine learning techniques can be an effective tool for financial risk management and recommends that financial institutions should consider using these techniques to manage financial risks more effectively and efficiently. The research also highlights the importance of proper risk management practices, including the identification, assessment, and management of risks, as well as the need for regular review and updating of risk management policies and strategies. The research suggests that regulators should provide clear guidelines and standards for risk management practices and ensure that financial institutions comply with them. However, the research also acknowledges the limitations and challenges of using machine learning techniques in financial risk management, such as data quality, model interpretability, and ethical considerations. The paper suggests that further research is needed to address these issues and to develop more sophisticated machine-learning models that can better capture and predict financial risks. Overall, the work gives useful insights into the potential of machine learning approaches in financial risk management and emphasises the importance of ongoing research and development in this field.