Abstract: A method for identifying fraudulent cryptographic currency transactions using a deep neural network includes: receiving, by a receiver of a processing server, a source dataset, the source dataset including labeled source data associated with a plurality of source features and being associated with a source domain; receiving, by the receiver of the processing server, a target dataset, the target dataset including unlabeled target data associated with a plurality of target features and being associated with a target domain; combining, by a processor of the processing server, at least a subset of the plurality of source features and at least a subset of the plurality of target features into a combined data layer; training, by the processor of the processing server, a deep neural network using a domain adaptation algorithm and the combined data layer to identify a set of final features.

Abstract: A method for scoring authenticity of a non-fungible token (NFT) using multiple, disparate data sets includes: receiving a scoring request for the NFT; determining a marketplace authenticity score for a marketplace where the NFT is available for purchase based on marketplace metrics; determining a visual authenticity score based on a comparison of visual features of the NFT to visual features of trusted NFTs; determining a wallet authenticity score for a blockchain wallet associated with ownership of the NFT based on a transaction history for the blockchain wallet; calculating a confidence score for the NFT based on a combination of the marketplace, visual, and wallet authenticity scores, the confidence score representing a likelihood that the NFT is authentic; and transmitting the calculated confidence score in response to the received scoring request.

Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant.

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.