Cybersecurity in Digital Finance: Artificial Intelligence-Powered Fraud Detection and Risk Management

The integration of cybersecurity in digital finance is essential to safeguard transactions and prevent financial fraud. Artificial intelligence (AI) plays a crucial role in enhancing fraud detection and risk management, ensuring secure online banking operations. Traditional fraud detection methods rely on rule-based approaches and supervised learning, which struggle to adapt to evolving cyber threats and often produce high false-positive rates. To address these limitations, this study proposes a Reinforcement Learning-based Fraud Detection System (RL-FDS) for online banking platforms. RL- FDS leverages deep reinforcement learning, anomaly detection, and real-time adaptive threat response to enhance fraud detection accuracy and minimize false alarms. The proposed system continuously learns from transaction patterns, improving decision-making without manual intervention. Experimental results demonstrate that RL-FDS significantly reduces fraud detection time while increasing accuracy, offering a robust and intelligent solution for digital finance security. This framework enhances financial cybersecurity by effectively countering sophisticated fraud techniques with AI-driven risk management.