Data Science Approaches for fraud Detection and Prevention

Ali Taghavirashidizadeh¹

1) Department of Electrical and Electronics Engineering, Islamic Azad University, Central Tehran Branch (IAUCTB)

Publication : 5th. International Congress On Engineering, Technology and Applied Sciences(etas2023.com)
Abstract :
Fraud detection and prevention have become critical challenges for organizations across various industries. Traditional rule-based systems and manual approaches are no longer sufficient in dealing with the increasingly sophisticated and evolving nature of fraud. Data science approaches have emerged as powerful tools in detecting and preventing fraudulent activities. This paper provides an overview of data science approaches for fraud detection and prevention. begins by discussing the importance of fraud detection and prevention in today s digital landscape. It then explores the role of data science in tackling fraud, highlighting the benefits of using advanced analytics techniques, machine learning algorithms, and artificial intelligence for fraud detection. Various data sources, such as transactional data, customer behavior data, and external data, are examined in the context of fraud detection. the paper delves into the different data science techniques used in fraud detection, including anomaly detection, predictive modeling, network analysis, and text mining. It discusses the strengths and limitations of each technique and explores their application in different fraud detection scenarios. also emphasizes the importance of data preprocessing and feature engineering in fraud detection, as well as the need for continuous monitoring and adaptive modeling to keep up with evolving fraud patterns.
Keywords : Keywords: Fraud detection data science Probability and statistics