Abstract: Methods and server systems for detecting fraudulent transactions are described herein. Method performed by server system includes accessing base graph including plurality of nodes further including plurality of labeled nodes and unlabeled nodes. Method includes assigning via Graph Neural Network (GNN) model, fraudulent label or non-fraudulent label to each unlabeled node based on the base graph. This assigning process includes generating plurality of sub-graphs based on splitting the base graph and filtering these sub-graphs via Siamese Neural Network model based on pre-defined threshold values. Then, the GNN model generates plurality of sets of embeddings based on plurality of filtered sub-graphs. Further, aggregated node embedding is generated for each node and then, final node representation for each node is generated via dense layer of GNN model. Then, fraudulent label or the non-fraudulent label is assigned to each unlabeled node of plurality of unlabeled nodes based on final node representation.

Abstract: Embodiments provide methods and systems for predicting overall account-level risks of cardholders. The method performed by server system includes accessing payment transaction data associated with a cardholder from a transaction database. Method includes generating a set of transaction features based on a set of transaction indicators. The method includes determining a plurality of network risk scores associated with the cardholder based on the set of transaction features and a set of trained machine learning models. The plurality of network risk scores includes a payment capacity risk score, a contactless payment risk score, and a set of account-level risk scores. The method includes aggregating the plurality of network risk scores to calculate an overall account risk score associated with the cardholder based on a statistical model. The method also includes transmitting a notification to the issuer server associated with the cardholder based on the overall account risk score.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.