AI-Driven Fraud Analytics

Fraud and cybercrime are evolving rapidly, exploiting technological diversity and human vulnerabilities. Traditional detection systems are reactive and limited against sophisticated threats such as phishing, deepfakes, and insider fraud. This chapter explores AI-driven fraud analytics as a transformative approach to predict, detect, and prevent cyber threats in real time. It highlights how machine learning, deep learning, natural language processing, and graph analytics collectively improve anomaly detection, fraud prediction, and decision speed. Case studies from JPMorgan Chase (2024) and Airtel (2025) demonstrate AI's practical efficiency, achieving up to 96% accuracy and reducing fraud recurrence by 38%. The chapter also addresses ethical and regulatory issues, emphasizing explainability and data privacy. Finally, it proposes a roadmap for AI integration into national and organizational fraud prevention strategies, promoting resilience, accountability, and sustainable digital security.