A one step further approach to fraud detection

Abstract

This paper presents a new approach to fraud detection that goes beyond traditional methods by incorporating a broader range of data sources and advanced machine learning techniques. The proposed framework integrates transactional data, social media activity, and network analysis to identify suspicious patterns and anomalies indicative of fraudulent behavior. We introduce a novel ensemble learning model that combines the strengths of various algorithms, including deep learning, support vector machines, and decision trees, to enhance detection accuracy and reduce false positives. Experimental results on a real-world dataset demonstrate the effectiveness of our approach in outperforming existing fraud detection systems. The study also explores the interpretability of the model's decisions, providing insights into the factors contributing to fraud and enabling better risk management strategies.