Year: 2021
Type: article
Abstract: The objective of this work is to develop an Intelligent Credit Card Fraud Detection System which can detect and prevent online credit, card fraud.Hidden Markov Model was applied in determining the spending habit or profile of credit card holders.And with the spending profile established, it becomes possible to determine if an incoming transaction from a card holder is fraudulent or not by comparing any new transaction with the credit card holder's spending history.Any deviation from the actual spending habit is seen as a probable fraud and will be restricted while further verification is carried out.The methodology adopted for this work is Structured System Analysis and Design Methodology (SSADM) and the programming language used is PHP-MYSQL.The system developed can be used by any bank or financial institutions dealing on credit cards to detect and prevent all kinds of credit card fraud as it has been found to be capable of detecting such possible online fraud. Background of the StudyA secured and trusted inter-banking network for electronic commerce requires high speed verification and authentication mechanisms that allow legitimate users easy access to conduct their business, while thwarting fraudulent transaction attempts by others.Fraudulent electronic transactions are already a significant problem, one that will grow in importance as the number of access points in the nation's financial information system grows.(Stolfo et al., 1998).The popularity of on-line shopping is growing day by day.According to an AC Nielsen study conducted in 2005, one-tenth of the world's population is shopping on-line.In today's increasingly electronic society and with the rapid advances of electronic commerce on the Internet, the use of credit cards for purchases has become convenient and necessary.
Institutions: Nnamdi Azikiwe University, Federal Polytechnic Oko
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3134024005