Construcción de Modelos Predictivos para Clasificar Transacciones Legitimas o Fraudulentas Utilizando Algoritmos de Aprendizaje Automático

Abstract

In the current era, the nature of transactions has evolved dramatically, shifting from traditional face-to-face interactions between cardholders and merchants to digital transactions through various channels, such as mobile applications, virtual banking, digital wallets, and electronic payment systems. This diversification has increased susceptibility to various forms of fraud, such as phishing, skimming, fraud by acquaintances, impersonation, and theft. These challenges have driven the need to implement more advanced tools than traditional rules to detect and prevent fraud. In this work, we address this challenge by proposing an approach based on supervised machine learning models. These models aim to detect fraudulent transactions in real-time or near real-time, minimizing false positives and alerting or declining transactions with a high likelihood of fraud.