Abstract: A method for facilitating fraud detection based on collaborative filtering is provided. The method includes segregation of payment modes into payment mode clusters and merchants into merchant clusters based on first historical transaction data associated therewith. Further, a collaborative filtering (CF) matrix is generated based on the payment mode clusters, merchant clusters, and second historical transaction data. Additionally, CF features are created for each payment mode cluster-merchant cluster pair based on the CF matrix and second historical transaction data. A risk score machine-learning model (ML) is trained based on created CF features and non-CF features. The risk score ML model is operable to classify a transaction request to one of a fraudulent transaction request and legitimate transaction request based on the training.

Abstract: Embodiments provide artificial intelligence-based methods and systems for generating scalable task-agnostic embeddings for training task-specific models. Method performed by server system includes accessing historical transaction data from database. Historical transaction data includes entity-specific data, cardholder-specific data, and transaction-specific data. Method includes generating one or more pseudo-objective models for each of plurality of entities based on historical transaction data and one or more pseudo-objectives. Plurality of entities includes acquirer, merchant, and issuer. Method includes determining via one or more pseudo-objective models, entity-specific embeddings for each entity of plurality of entities based on entity-specific data. Entity-specific embeddings includes acquirer-specific embeddings, merchant-specific embeddings, and issuer-specific embeddings. Upon receiving request to induct new entity, method includes accessing new entity data associated with new entity from database.