Data Science Approaches for fraud Detectin and Preventin

Fraud detectin and preventin have become critial challenges for organizatins across variousindustries. Traditinal rule-based systems and manual approaches are no longer suffient indealing with the increasingly sophistiated and evolving nature of fraud. Data scienceapproaches have emerged as powerful tools in detectig and preventig fraudulent actiitis.This paper provides an overview of data science approaches for fraud detectin and preventin.begins by discussing the importance of fraud detectin and preventin in today's digitallandscape. It then explores the role of data science in tackling fraud, highlightig the benefis ofusing advanced analytis techniques, machine learning algorithms, and artiiial intelligence forfraud detectin. Various data sources, such as transactinal data, customer behavior data, andexternal data, are examined in the context of fraud detectin. the paper delves into thediffrent data science techniques used in fraud detectin, including anomaly detectin,predictie modeling, network analysis, and text mining. It discusses the strengths andlimitatins of each technique and explores their applicatin in diffrent fraud detectinscenarios. also emphasizes the importance of data preprocessing and feature engineering infraud detectin, as well as the need for contiuous monitoring and adaptie modeling to keepup with evolving fraud pattrns.

Keywords:

Fraud detectin , data science , Probability and statitis