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 method, apparatus, system, and computer program code for dynamically modeling multi-tenant data in a machine learning platform. A recommendation engine receives a first data set from a user. The recommendation engine characterizes the first data set to determine data attributes and data characteristics of the first data set. The recommendation engine aligns the data attributes of the first data set with a second data set according to an ontology. Based on the data characteristics of the first data set, the recommendation engine identifies a set of pre-trained models that was trained from training parameters selected from data attributes and data characteristics of a second data set. The recommendation engine recommends the set of pre-trained models to the user.

Abstract: Disclosed is a method and system for assessing the authenticity of a communication. The method comprises receiving data of the communication by the processor between one or more participants. Further, extracting one or more features by the processor from the data by using data extraction techniques. Further, comparing the one or more features by the processor with predefined threshold features stored in a feature repository. Further, generating, one or more authenticity attributes by using one or more trained Artificial Intelligence (AI) models applied over the one or more features, along with results of the comparing. Each of the one or more authenticity attributes generates a recommendation output, providing the authenticity of the communication.

Abstract: Disclosed is a method and system for assessing the authenticity of a communication. The method comprises receiving data of the communication by the processor between one or more participants. Further, extracting one or more features by the processor from the data by using data extraction techniques. Further, comparing the one or more features by the processor with predefined threshold features stored in a feature repository. Further, generating, one or more authenticity attributes by using one or more trained Artificial Intelligence (AI) models applied over the one or more features, along with results of the comparing. Each of the one or more authenticity attributes generates a recommendation output, providing the authenticity of the communication.