A Prototype for Credit Card Fraud Management

Abstract

      Advancements in analytics are transforming the credit card industry, making it crucial to maintain consumer confidence and protect digital transactions' security. This study examines many methods for spotting credit card fraud and weighs the advantages and disadvantages of each. It also highlights how important accurate credit risk assessment is to financial institutions’ capacity to predict defaults and reduce potential losses.

The SPEED project is a proactive decision-making initiative that leverages event-driven approaches to capitalize on opportunities and anticipate issues. Its machine learning component adapts to changing fraudulent tactics, identifying fraud tendencies quickly. Fraud analysts benefit from the system's user interface, allowing informed decision-making based on automated processing.

To stop typical forms of credit card fraud, such as the unauthorized use of virtual terminals or postal orders, effective fraud detection is essential. In this study, novel assessment criteria that search for recurrent patterns in the data are used to evaluate decision trees employing support vector machines (SVMs) for fraud detection. This novel strategy effectively mitigates the cold-start issue while addressing issues with data imbalance and variety and achieving performance levels close to top models.

As credit card transactions continue to evolve, these advancements in fraud detection and risk assessment are critical to enhancing the security and reliability of financial systems