Abstract: Embodiments provide methods and systems for temporal graph representation learning based on node-level temporal point processes. Method performed by the server system includes accessing historical interaction data, generating temporal graph based on historical transaction data and predicting likelihoods of future interaction occurrences among entities based on a pre-trained TPP based graph neural network (GNN) model. Method includes determining edge embeddings of each node based on node features of each node and direct neighbor nodes of each node. Method includes generating edge-contextualized node embeddings of each node corresponding to the edge embeddings based on a neural network model and computing a likelihood of future interaction occurrences associated with the each node based on edge-contextualized node embeddings and a conditional intensity function.

Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score.

Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score.