Methods and systems for predicting fraudulent transactions based on acquirer-level characteristics modeling

A server system comprising:

• at least one processor;
• and
• a memory storing computer-executable instructions thereon, which when executed by the at least one processer, cause the at least one processor to perform the operations of:
• accessing historical transaction data of payment transactions associated with an acquirer server from a transaction database,
• determining acquirer features associated with the acquirer server and transaction features associated with an individual payment transaction based, at least in part, on the historical transaction data,
• generating, via an embedding layer, a latent representation corresponding to the individual payment transaction based, at least in part, on the acquirer features and the transaction features, and
• training a fraud classifier and an acquirer classifier based, at least in part, on the latent representation and a multi-component event-aware loss function, wherein the training is performed by executing a plurality of steps, the plurality of steps comprising:
• computing, by the server system, the multi-component event-aware loss function based, at least in part, on execution of the fraud classifier and the acquirer classifier, and
• updating, by the server system, network parameters of the fraud classifier, the acquirer classifier, and the embedding layer based, at least in part, on the multi-component event-aware loss function.