Abstract: Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer-implemented methods and computer program products are also provided.

Abstract: A method of determining fraud includes: receiving a transaction request associated with a first payment transaction between a merchant and a user from a merchant system; generating a first risk score based on the transaction request and a first set pot of transaction data received prior to the transaction request; processing a transaction request approval based on the first risk score not satisfying a first threshold; receiving a risk score request associated with the first payment transaction, where the risk score request is received after the transaction request has been approved; generating a second risk score based on a second set of transaction data received after the first risk score is determined; and automatically classifying the first payment transaction as potentially fraudulent in response to determining that the second risk score satisfies a second threshold.

Abstract: Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer implemented methods and computer-program products are also provided.

Abstract: A dynamic next-stop or next-item recommendation system that is built entirely from raw card transaction data logs. These data logs contain rich transaction data between cardholders and merchants. A query network approach is constructed for geometrical expressivity and automatically learns the inherent class-hierarchy. To ensure scalability and interpretability of the approach, merchants or entities are grouped into interpretable categories and propose a quadtree-based spatial decomposition of the underlying geography. A two-step recommendation process initiates: (1) predict next-merchant quadtree-box and category combination (2) recommend merchants within the predicted combination. This novel neural architecture may handle the hierarchical classification task in the first part of the recommendation system and compare the methods to previous state-of-the-art approaches in related areas.

Abstract: A method of determining fraud includes: receiving a transaction request associated with a first payment transaction between a merchant and a user from a merchant system; generating a first risk score based on the transaction request and a first set pot of transaction data received prior to the transaction request; processing a transaction request approval based on the first risk score not satisfying a first threshold; receiving a risk score request associated with the first payment transaction, where the risk score request is received after the transaction request has been approved; generating a second risk score based on a second set of transaction data received after the first risk score is determined; and automatically classifying the first payment transaction as potentially fraudulent in response to determining that the second risk score satisfies a second threshold.

Abstract: The system and method may assess the merchant risk level on a more continuous scale rather than a binary categorization. It may produce a continuous risk score proportional to the likelihood of a merchant being risky, effectively addressing the issue of shades of gray encountered by the traditional blacklisting approach. The continuous risk score feature provides greater flexibility as it allows the payment network to make dynamic pricing decisions (known as interchange optimization) based on the merchant risk level. Using collective intelligence from transactions across the payment network, the system and method may be able to assess the merchant risk level with high accuracy. The system and method may be particularly beneficial to small merchants with low transaction volume as even a few fraudulent transactions can easily put them in the high-risk merchant category. Further, the system and method may help payment processing networks make better decision on cross-border transactions.

Abstract: Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer implemented methods and computer-program products are also provided.

Abstract: A dynamic next-stop or next-item recommendation system that is built entirely from raw card transaction data logs. These data logs contain rich transaction data between cardholders and merchants. A query network approach is constructed for geometrical expressivity and automatically learns the inherent class-hierarchy. To ensure scalability and interpretability of the approach, merchants or entities are grouped into interpretable categories and propose a quadtree-based spatial decomposition of the underlying geography. A two-step recommendation process initiates: (1) predict next-merchant quadtree-box and category combination (2) recommend merchants within the predicted combination. This novel neural architecture may handle the hierarchical classification task in the first part of the recommendation system and compare the methods to previous state-of-the-art approaches in related areas.