Analytical Study On Prevention And Detection Of Financial Cybercrime And Frauds Using Transaction Pattern Generation Tool

Abstract

E-commerce is a vital sales avenue for multinational businesses in today's technology environment. Due to the fast growth of e-commerce, credit card sales have increased. Unfortunately, criminals have profited from credit card theft. The discovery of safety faintness in standard credit card dispensation schemes has increased credit card theft, costing billions of dollars yearly. Modern credit card thieves are agile and use cutting-edge tactics. Global fraud complicates credit card issues for banks and other financial businesses. Many techniques, such as First Virtual, Cyber Cash, and SET, are employed to avoid financial cybercrime. Although customers and businesses rarely use these systems, they are very secure. These models protect our online transactions, but they cannot prevent fraud if a customer's credit card information is physically lost or falls into the wrong hands. The study is distinctive in that it uses data mining, statistics on one stage for modeling portion. Effort detailed in thesis necessity be beneficial to academics; in particular, a literature review of data mining techniques is an effort to offer a roadmap for the researchers to explore and choose the best data mining approach before putting it into practice. Additionally, building additional financial applications benefits from an considerate of the role data mining plays in detecting economic misconduct.
Although the programme was developed with online transactions in mind, cardholders can also use it for offline transactions.