Artificial Intelligence and Machine Learning in Fraud Detection: Practical Applications, Predictive Models, and Ethical Risks

Artificial intelligence (AI) and machine learning (ML) are reshaping fraud detection by enabling continuous auditing, predictive modeling, and real-time anomaly identification. Unlike traditional auditing methods limited by manual sampling, AI-based systems process vast data streams and uncover subtle irregularities across financial and operational records. Practical applications include credit-card fraud detection, procurement monitoring, and journal-entry analysis, where supervised and unsupervised models—augmented by graph-based approaches—detect anomalies and adapt to adversarial behaviors. While these techniques enhance efficiency and detection accuracy, they also raise significant ethical and governance concerns, particularly regarding algorithmic bias, transparency, privacy, and regulatory compliance. A balanced approach combining technological advances with ethical safeguards, model interpretability, and robust governance structures is essential to realize the benefits of AI in fraud detection. This integration promises to shift auditing from episodic assurance toward continuous, adaptive systems that strengthen organizational resilience and public trust.