Mastercard Framework for Extortion Discovery Utilizing AI Methods

Abstract

The extortion of Mastercard is a major concern for barnks and credit card companies worldwide, and it is estimated that fraud losses amount to billions of dollars per year. False transactions damage the reputation of financial institutions and cause financial losses. Therefore, identifying fraudulent transactions is essential to the financial industry's survival. In this paper, we propose a system for identifying Mastercard misrepresentations that uses artificial intelligence (Al) techniques to classify exchanges as false or authentic. The suggested methodology relies on the analysis of value-based data, highlighting elements like area, time, and exchange sum, and employs Al computations to identify bogus cases. We demonstrate the feasibility of the suggested approach by testing it on a certified Mastercard deception dataset, achieving high review rates and precision.