## 1 Introduction  
Transaction fraud affects different sectors of society, from individuals who use online services to companies and businesses that sometimes have to absorb the economic costs due to these illegal operations, in addition to the reputational damage they entail. This problem generates significant economic losses worldwide and has increased with the advance of the digital age. Therefore, this work focuses on digital fraud committed on online payment platforms. This activity typically arises from the illicit use of credit and debit cards obtained unlawfully. Another contributing factor is unauthorized transactions resulting from unauthorized access aimed at stealing funds from user accounts. In light of this, fraudulent activities can be detected at transaction and user account levels. At the account level, this generally includes transactions and other actions that fraudulent users might carry out on online platforms. Traditionally, digital fraud solutions have been tackled from various angles, including the use of machine learning rules and algorithms [1, 2] that can effectively prevent some of the most common cases. Fraud detection is a complex issue that encompasses several challenges. For instance, it falls under the category of imbalanced class problems, leading some approaches to address it through class weighting [2]. The camouflage issue arises when fraudsters imitate normal behavior to execute fraudulent activities [3]. The effectiveness of detection also heavily relies on the size of the data used [4]. Moreover, it is a dynamic process where uncovering hidden patterns can significantly aid in prevention [5]. However, many machine learning algorithms fail to account for the full context in which a transaction occurs. The context in which a fraudulent transaction occurs is crucial for identifying relevant characteristics useful for its detection. Some features are more significant than others and can play a crucial role in differentiating fraudulent transactions from legitimate transactions [6]. Users' different actions within a digital platform contribute to this context. Within these, intrinsic characteristics of the transaction can be found, such as amounts, types of card, dates [1], and even patterns related to user behavior such as the frequency and recency of transactions [6], as well as the temporality about the dynamics of the problem [7]. All these characteristics offer a broader view whose purpose will always focus on distinguishing the types of operations. This work applies a temporal graph network (TGN) algorithm to predict fraudulent events using real data from an online payment platform. The TGN algorithm operates with an Event-Based Temporal Graph (ETG), a graph representation for a dynamic continuous approach (not a discrete approach, nor does it use temporal snapshots of a graph). This means that one of the main contributions of this work is the construction of an ETG based on real data, which serve as the input for the TGN algorithm. The data were provided by Moneypool, an online payment platform with clients worldwide but with a more significant presence in Mexico. This dataset is of utmost importance as it represents a sample of transactions in Latin America, particularly from Mexico. Latin America is the region with the highest rate of credit card fraud in the world [8], so the availability of this kind of information in a work of this type represents a contribution to the use and development of tools to attack this problem. As of the time of writing this work, only [9] uses real transaction datasets with a TGN methodology. Thus, one of the main purposes is to evaluate how adding different types of events influences the metric results. The contributions of this work are highlighted in the following lines:  
* The proposal of an algorithm with a TGN approach, incorporating explicit training, validation, and testing steps, with a focus on fraud detection.