ABSTRACT
Detecting credit card theft is a crucial process to safeguard customers from unauthorized charges. By utilizing data science and machine learning, a model is developed using past fraudulent transactions as examples. This model is then employed to analyze fresh credit card transactions in real-time. Its goal is to reliably detect fraudulent activities while reducing the amount of legal transactions incorrectly marked as fraudulent. Through continuous refinement and updates, credit card companies can stay ahead of fraudsters and ensure a secure experience for their customers. By striking the right balance between precision and sensitivity, the system can effectively identify fraudulent transactions without causing unnecessary inconvenience to users. This way, credit card firms can protect their customers’ financial interests and maintain trust in their services.
