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: A phase-locked loop (PLL) is implemented to have another (second) PLL in place of the controlled oscillator. When a known frequency change in the frequency of the output clock is desired, in addition to changing a configuration of the PLL (first PLL), the configuration of the second PLL is also changed to cause the frequency of the output clock to change quickly. In various embodiments, the configuration of the second PLL is changed by changing the divisor of the feedback divider of the second PLL, the divisor in a pre-scaler in the second PLL, the control voltage of a VCO used in the second PLL, and any other point of user control in the second PLL.

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.