In a rapidly evolving financial industry, the integration of AI and machine learning has brought about significant advancements, such as establishing new standards for efficiency, precision, and innovation. This transformation has revolutionised decision-making and operational processes within markets and institutions. However, it has also presented challenges, including speculative risk, bias, and security concerns. This article examines their impact on fraud detection, credit scoring, and algorithmic trading allowing for a better understanding of their potential benefits and inherent risks. Additionally, these insights are crucial for addressing the social and ethical implications to ensure a balanced and responsible adoption of AI and machine learning within the financial industry.