Leveraging Financial Data and Risk Management in Banking sector using Machine Learning

In today's dynamic financial environment, the capacity to effectively process and analyse extensive datasets is crucial for making well-informed investment choices and minimizing potential risks. To effectively analyse, predict, and mitigate risks, this research paper applies sophisticated machine learning (ML) methods to the intricate field of monetary risk in the banking sector. The research explores various risk aspects, including credit, market, and operational risks, with the aim of contributing to the ever-evolving field of machine learning applications in finance. The goal of the study is to increase the stability of financial institutions and enhance the accuracy of risk assessment by utilizing large-scale datasets and sophisticated machine learning modes. The performance of the proposed machine learning models is tested and validated on a benchmark dataset. The Catboost model achieves higher accuracy after fine-tuning through Optuna.