Credit Card Fraud Detection and Prevention Using a Formula-Based HMM Model Authentication System for Secure Transaction

Credit card fraud is a persistent problem in the financial sector, despite the implementation of various cybersecurity measures. In this study, we propose a novel Hidden Markov Model-based approach (HMM) for analyzing user behavior and a formula-based authentication system for verification for identifying credit card fraud. The HMM is trained on recent transaction data to identify patterns in user behavior, and any deviation from the expected behavior pattern triggers the formula-based authentication system. Our proposed model aims to provide an effective solution to mitigate credit card fraud while providing a seamless user experience. We assess the effectiveness of the suggested solution using a number of criteria, such as accuracy, false-positive rate, false-negative rate, and processing time, and our evaluations demonstrate potential reductions in credit card fraud. Our proposed solution provides a strong defense against credit card fraud. The HMM model utilizes sophisticated machine learning algorithms to detect abnormalities in user behavior patterns, making it an effective tool for detecting fraudulent activity. Furthermore, our formula- based authentication system requires users to answer simple mathematical questions; this helps ensure that only legitimate users are able to access the system. In addition, the processing time of our proposed solution is relatively low, thereby providing a seamless user experience without compromising security. Finally, our solution can accurately detect credit card fraud while producing minimal false positives and false negatives. Our results demonstrate that our solution can be an effective and efficient tool for mitigating credit card fraud.