Artificial Intelligence in Finance: Risk Prediction, Fraud Detection, and Algorithmic Trading

Abstract

Artificial Intelligence (AI) is fundamentally transforming the financial services industry by enabling sophisticated risk prediction, real-time fraud detection, and high-frequency algorithmic trading. This paper presents a comprehensive examination of three dominant AI applications in modern finance: machine learning-based credit and market risk assessment models, deep learning-driven anomaly detection systems for financial fraud prevention, and reinforcement learning-powered algorithmic trading strategies. Drawing on a rigorous mixed-methods research framework combining systematic literature review, quantitative performance analysis, and an empirical case study of an AI-integrated fintech platform, this study demonstrates that AI-driven financial systems consistently outperform traditional rule-based and statistical approaches. Risk prediction models achieve AUC scores exceeding 0.94, fraud detection systems reduce false positive rates by over 62%, and AI trading algorithms generate risk-adjusted returns surpassing benchmark indices by 18–27%. Future directions encompass Explainable AI (XAI), federated learning, and quantum-enhanced optimization. Findings affirm that responsible AI deployment in finance yields transformative gains in accuracy, efficiency, and risk management.