Abstract: Methods and server systems for performing similarity-search are described herein. Method performed by server system includes accessing entity-related dataset and using that generating set of entity-specific features including subset of numerical features and subset of categorical features corresponding to each of plurality of entities. Method includes determining label corresponding to each of plurality of entities based on subset of categorical features. Method includes generating via first model, first set of entity-specific embeddings corresponding to each of plurality of entities based on corresponding subset of numerical features and corresponding label.

Abstract: Embodiments provide methods and systems for unsupervised representation learning for bipartite graphs. Method performed by server system includes accessing historical transaction data from database. Method includes generating a bipartite graph based on historical transaction data. Bipartite graph represents a computer-based graph representation of a plurality of cardholders as first nodes and a plurality of merchants as second nodes and payment transactions between first nodes and second nodes as edges. Method includes sampling direct neighbor nodes and skip neighbor nodes associated with a node based on neighborhood sampling method and executing direct neighborhood aggregation method and skip neighborhood aggregation method to obtain direct neighborhood embedding and skip neighborhood embedding associated with node, respectively.

Abstract: Embodiments provide methods and systems for predicting chargeback behavioral data of an account holder. The method performed by a server system includes accessing payment transaction data associated with the account holder from a transaction database. The payment transaction data includes a set of transaction indicators corresponding to payment transactions performed by the account holder within a predetermined time period. The method further includes generating a set of transaction features based on the set of transaction indicators. Furthermore, the method includes computing, via a chargeback risk prediction model, a set of chargeback risk probability scores corresponding to one or more time intervals associated with the account holder based, at least in part, on the set of transaction features. The method also includes transmitting a notification to an issuer server associated with the account holder based, at least in part, on the set of chargeback risk probability scores.