Securing Fintech: A Machine Learning Approach for Credit Card Fraud Detection

This research addresses the rapidly growing need for advanced detection systems as a response to the simultaneous increase in fraudulent activity that has accompanied the increasing usage of credit cards, particularly in the digital era. The ever-increasing difficulties brought about by the growing incidence of credit card theft highlight the importance of taking preventative steps to guarantee the security of financial transactions, particularly in the context of the ever-changing environment of online payment systems. The proposed solution is based on a strategy that places an emphasis on machine learning, with a specific concentrate on logistic regression. This approach has been recommended as a particular solution. Utilising the Flask framework, this research incorporates logistic regression into a user-friendly fraud detection status screen that is displayed on a webpage. The accuracy of the suggested logistic regression is 88.15%. This screen is displayed on the webpage. The purpose of this study is to dramatically improve the accuracy and efficiency of fraud detection methods by integrating online integration with logistic regression in a manner that is synergistic. This technique not only addresses the existing problems of credit card theft, but it also presents a forward-thinking strategy to foresee and prevent future dangers. As a result, it makes a significant contribution to the overall reinforcement of digital security and resilience in this technology-driven era.