Abstract
In the field of finance, machine learning has become a potent instrument that is transforming conventional methods of data analysis, decision-making, and risk management. This study examines how machine learning techniques are applied in the financial sector, discussing the challenges and opportunities of machine learning in the financial sector. Machine learning algorithms have been successfully used in fields including stock market forecasting, credit risk assessment, fraud detection, algorithmic trading, and portfolio optimization by utilising enormous volumes of financial data. However, issues with model robustness, interpretability, data quality, and regulatory compliance continue to be major roadblocks. By analyzing the applications, identifying challenges, and exploring opportunities for further development, this chapter seeks to contribute to the understanding and advancement of machine learning in the financial sector.
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