The Use of Artificial Intelligence in Detecting Financial Fraud: Legal and Ethical Considerations

Abstract

Financial fraud poses a significant threat to global financial systems, exacerbated by the digitalization of financial services that have broadened the scope for cyber-enabled fraud, such as phishing and ransomware. As traditional methods struggle to keep up with evolving fraud tactics, Artificial Intelligence (AI) has emerged as a powerful tool in detecting and preventing financial fraud. AI systems, particularly those employing machine learning (ML) algorithms, can analyze vast amounts of data in real time, identifying patterns and anomalies indicative of fraudulent activities. The integration of AI into financial systems enhances risk management, decision-making, and compliance with anti-money laundering (AML) regulations. However, the deployment of AI in finance raises critical legal and ethical challenges. Regulatory frameworks often lag behind technological advancements, creating gaps that complicate compliance. Issues of algorithmic bias, transparency, and the interpretability of AI decisions also pose significant concerns, particularly in sensitive areas like fraud detection. This paper explores the transformative impact of AI on financial fraud detection, focusing on the legal, ethical, and technical challenges that accompany its deployment. It highlights the need for adaptive legal frameworks, enhanced transparency, and ethical safeguards to ensure the responsible use of AI in finance. Future research directions are recommended to address these issues, ensuring that AI's benefits in fraud detection are maximized while minimizing potential risks.