Fraudulent Event Detection via Temporal Graph Networks

Diego Saldana Ulloa

IEEE

julio 2025

The development of new objective functions in Neural Architecture Search (NAS) may include temporal and dynamic aspects of the problem to be modeled. In this line, we show the application of a temporal graph neural algorithm for detecting fraud on real data of an online payment platform, which is made up of different events that a user can perform, such as card registration, device registration, bank account registration, and IP registration. A combination of previous information generates different Event-Based Temporal Graphs (ETG) used in constructing the objective function for our neural network. The results show that combining different events effectively impacts AUC and recall, which is directly related to the proportion of fraudulent observations captured, and that there is a direct correlation between better metrics values and the graph density (measured as the proportion between vertices and edges). Few works focus on applying ETGs for fraud detection using real data and its analysis through the chara

A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms

Diego Saldana Ulloa

MDPI

diciembre 2024

A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph (ETG) were sourced from an online payment platform and include details such as users, cards, devices, bank accounts, and features related to all these entities. Based on these data, seven distinct graphs were created; the first three represent individual interaction events (card registration, device registration, and bank account registration), while the remaining four are combinations of these graphs (card–device, card–bank account, device–bank account, and card–device–bank account registration). This approach was adopted to determine if the graph’s structure influenced the detection of fraudulent transactions. The results demonstrate that integrating more interaction events into the graph enhances the metrics, meaning graphs c

A Process for Topic Modelling Via Word Embeddings

Diego Saldana Ulloa

Research in Computing Science

septiembre 2023

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called UMAP is used. This method manages to reduce the dimensions while preserving part of the local and global information of the original data. K-Means is used as the clustering algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF statistics, Topic Diversity, and Topic Coherence to get the meaning of the words on the clusters. The results of the process show good values, so the topic modeling of this process is a viable option for classifying or clustering texts without labels.

La publicidad engañosa: el dilema ético de la Mercadotecnia Educativa en las Universidades Mexicanas

Leonel De Aquino Martin

Liderazgo Ético y Sostenibilidad: Desafíos Estratégicos en la Alta Dirección del Siglo XXI

En México las universidades enfrentan un gran reto: no solo deben formar ciudadanos capaces de desenvolverse en una sociedad del conocimiento, sino también vender su oferta educativa en un mercado cada vez más competitivo, para lograrlo, muchas instituciones han adoptado la mercadotecnia educativa como parte esencial de su modelo de gestión, buscando atraer y retener a un estudiante que hoy actúa como un consumidor informado, crítico y exigente