Data analytics to prevent retail credit card fraud: empirical evidence from Latin America

Abstract

Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies, especially in Latin American countries. This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods (data mining, machine learning and artificial intelligence). To achieve this objective, the study employs a predictive methodology using descriptive and exploratory statistics and frequency, frequency & monetary (RFM) classification techniques, differentiating between SMEs and large businesses via cluster analysis and supervised models. A dataset of 221,292 card records from a Latin American merchant payment gateway for the year 2022 is used. For fraud alerts, the classification model has been selected for small and medium–sized merchants, and the multilayer perceptron (MLP) neural network has been selected for large merchants. Random forest or Gini decision tree models have been selected as backup models for retraining. For the detection of punctual fraud patterns, the K-means and partitioning around medoids (PAM) models have been selected, depending on the type of trade. The results revealed that the application of the identified models would have prevented between 48 and 85% of fraud transactions, depending on the trade size. Despite the promising results, continuous updating is recommended, as fraudsters frequently implement new fraud techniques.