Exploring Data Science Techniques for Fraud Detection

Fraud detection has become increasingly critical as online transactions and digital interactions proliferate across industries. This paper explores advanced data science methodologies and machine learning algorithms for identifying fraudulent activities in real-time, addressing challenges like data imbalance, feature engineering, and model evaluation. It provides insights into traditional and emerging fraud detection techniques and emphasizes future directions for research and implementation. Key findings and ethical considerations are also discussed.