Exploring Techniques to Safeguard Credit Card Transactions: A Comprehensive Study on Fraud Detection and Prevention

Credit cards have greatly improved convenience in financial transactions, particularly in the realm of e-commerce, both online and offline. They enable cashless shopping worldwide, but this convenience has also led to an increase in credit card fraud over time. Addressing this issue involves two key considerations: detecting fraud and employing techniques to prevent it. While credit card payments are crucial and easy, they also pose risks due to the ease with which fraudulent transactions can occur. Credit card fraud is a significant global problem, resulting in substantial financial losses. This research delves into credit card fraud and explores methods to combat it. Various approaches, such as Data Mining, Genetic Algorithms, Hidden Markov models, and Neural Networks, are employed to detect and prevent fraud. The research assesses these data mining techniques to determine their effectiveness in fraud prevention. One noteworthy technique, the Hidden Markov Model (HMM), mimics cardholder behavior. It scrutinizes transactions by comparing them with card and cardholder profiles, flagging suspicious activities as potential fraud. Additionally, the research discusses the application of Support Vector Machines (SVM) to assess the creditworthiness of companies. The emphasis is placed on the accuracy of this assessment, particularly for companies facing bankruptcy. In summary, this research investigates credit card fraud and the techniques used to mitigate it, including Data Mining, Genetic Algorithms, Hidden Markov Model, and Neural Networks. It also evaluates these methods based on factors like accuracy, speed, and cost. Among these techniques, the Hidden Markov Model (HMM) stands out for its ability to analyze cardholder behavior and detect potential fraud.