The financial market in the U.S. is digitized, and it has transformed the operations and offered more opportunities to innovate, yet it has also exposed institutions to more cyber threats and fraud risks. Traditional fraud detection methods are now not sufficient to handle sophisticated fraud cases such as identity theft, account takeovers, and ransomware. The intersection of cybersecurity, artificial intelligence (AI), and fraud detection is critically reviewed in this research paper, which demonstrates how the AI-based solutions and machine learning and deep learning in particular can enhance the real-time identification and prevention of fraudulent activity. The research is based on a qualitative research design and secondary sources of information about the regulatory reports, academic literature, and industry analyses to evaluate the benefits and limitations of the introduction of AI into the financial systems of security. AI has been found to be extremely useful in terms of accuracy and resilience regarding detection, but poses ethical and legal challenges in terms of transparency, bias, and data privacy. The paper concludes that AI in fraud detection implementation must be sustainable in the sense of the need to balance technological innovation, regulatory compliance, and ethical protection.