Artificial intelligence and machine learning in finance: Enhancing efficiency, innovation and decision-making

The rapid Integration of Artificial Intelligence (AI) and Machine Learning (ML) into the financial industry is revolutionizing traditional financial practices, enhancing operational efficiency, fostering innovation, and improving decision-making. AI and ML technologies enable financial institutions to harness vast datasets, predict market trends, streamline processes, and provide personalized services, transforming key areas such as fraud detection, risk management, and algorithmic trading. This study explores the primary applications of AI and ML in finance, examining how they contribute to increased efficiency through automation and real-time data processing, the development of innovative financial products, and data-driven decision-making. Despite these advancements, challenges remain, including data privacy concerns, model interpretability, algorithmic bias, and the need for regulatory frameworks. A mixed-methods approach, combining literature review and case studies of industry practices, provides insights into both the opportunities and risks associated with adopting AI and ML in finance. The study concludes by emphasizing the need for ongoing research to improve AI systems' transparency, security, and ethical standards in financial services, ensuring their full potential is realized while safeguarding stakeholders' interests.