A Systematic Credit Card Analysis for Detection of Compromised Data Using Machine Learning

Credit card security is paramount for banks, especially during the pre-issuance phase. This paper examines the multifaceted security measures implemented by banks to protect credit cards and cardholder data before a card is even issued. We explore the vulnerabilities inherent in the card production and personalization processes, and analyze the various countermeasures employed to mitigate these risks. These include secure printing facilities, data encryption, EMV chip technology integration, and rigorous access controls. Furthermore, we discuss the importance of robust data security protocols for safeguarding sensitive information during application processing and account setup. This paper highlights the proactive approach taken by banks to minimize the potential for fraud and data breaches in the critical pre-issuance stage, ensuring the integrity and security of the credit card ecosystem. The findings emphasize the continuous need for vigilance and innovation in security practices to stay ahead of evolving threats and maintain customer trust.