Diego Saldana Ulloa

Director Data Science

Moneypool

diego.saldanaua@udlap.mx

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I am passionate about leveraging advanced technologies to solve complex problems. My expertise spans mathematical optimization, machine learning, and graph machine learning, where I continuously explore innovative approaches to enhance data analysis and interpretation. My academic background includes a bachelor’s and master’s degree in physics, providing me with a solid foundation in mathematical modeling. I have been involved in diverse projects, including cancer dose dosification, theoretical physics, user segmentation and fraud prevention using machine learning and graphs. Currently residing in Mexico, I balance my academic pursuits with practical experience in the industry, aiming to integrate both spheres to drive impactful solutions. My professional journey is marked by a commitment to excellence, continuous learning, and a keen interest in the intersection of academia and industry.

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Artículos (3)

Académico

Detección de fraude digital mediante el uso de grafos

Ciencia Ergo Sum

septiembre 2025

El fraude transaccional en plataformas digitales ha incrementado en años recientes. Los defraudadores utilizan diferentes técnicas para apropiarse de los recursos económicos de usuarios y sacar provecho de algunas vulnerabilidades. Por esta razón, se han implementado diferentes soluciones basadas en el aprendizaje automático para abordar el problema. En este trabajo se describen algunas características del fraude digital así como los principales métodos que se han utilizado para la detección de fraude. A su vez, la transición que ha existido hacia un enfoque que combina grafos con aprendizaje automático, ha propuesto las llamadas redes neuronales de grafos. Finalmente, se mencionan algunos retos del área relacionados a las características de los datos necesarios para operar este tipo de algoritmos.

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Académico

Fraudulent Event Detection via Temporal Graph Networks

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

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Académico

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

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

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