Automatic Card Fraud Detection Based on Decision Tree Algorithm

Abstract

This paper delves into the analysis of card fraud within the banking system. Its aim is to gain a comprehensive understanding of fraud in the banking sector and explore effective detection techniques. The paper examines advanced techniques such as data analysis, automatic learning algorithms, and real-time monitoring systems to detect suspicious patterns, anomalies, and deviations from normal behavior with precision. To achieve this, the research methodology employs a combination of qualitative and quantitative analysis. Furthermore, empirical research is conducted to evaluate the effectiveness of Machine Learning-based decision tree algorithms in identifying card fraud using real-world datasets. By understanding the nature of fraud and implementing robust detection methods, banks can safeguard their operations, assets, and customers, and uphold trust in the banking system.