Abstract: A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.

Abstract: A weighing station device, a computer-implemented method and a computer program product for estimating a weight of an animal. The weighing station device includes a stall for the animal, a camera in the stall that is activated to capture a plurality of digital images of the animal when a presence of the animal is detected at the stall. A processor obtains the plurality of digital images from the camera, constructs a three-dimensional digital image of the animal from the plurality of digital images, determines a dimension of the animal from the three-dimensional digital image, and estimates the weight of the animal from the determined dimension and a model relating the dimension of the animal to the weight of the animal.

Abstract: A weighing station device, a computer-implemented method and a computer program product for estimating a weight of an animal. The weighing station device includes a stall for the animal, a camera in the stall that is activated to capture a plurality of digital images of the animal when a presence of the animal is detected at the stall. A processor obtains the plurality of digital images from the camera, constructs a three-dimensional digital image of the animal from the plurality of digital images, determines a dimension of the animal from the three-dimensional digital image, and estimates the weight of the animal from the determined dimension and a model relating the dimension of the animal to the weight of the animal.

Abstract: Provided is a mechanism to evaluate and predict credit ratings of corporations from a transaction perspective (i.e., on a transaction-by-transaction basis) by integrating real-time event and external information such as, for example, social media and news reports. In various embodiments, such a mechanism may be implemented via systems, methods and/or computer program products.

Abstract: A computer implemented method, program product, and/or system allocate human resources to a cohort. At least one attribute held by each member of a group of human resources is identified. A request is received, from a planned cohort, for multiple human resources that collectively possess a set of predefined attributes, wherein no single human resource possesses all of the predefined attributes. The set of human resources that satisfies the request is identified and assigned to the planned cohort.

Abstract: A method for generating event profiles comprises receiving source data in a processor, extracting an attire property from the source data, extracting an event attribute from the source data, associating the attire property with the event attribute, generating an event profile that includes the associated attire property and the event attribute, and saving the event profile in a memory.

Abstract: A method of characterizing relationships among spatio-temporal events and a system to characterize the relationships are described. The method includes receiving information specifying the spatio-temporal events and associated categories from one or more sources. The method also includes building, using a processor, a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets. Each of the two or more SL and TL sets defines a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG. The respective (SL,TL)-neighborhood of each of the spatio-temporal events is a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories is a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.

Abstract: A system, method and program product are provided for calculating a routing scheme for a set of correspondent banking (CB) transactions. The described system includes: a network modeling system that creates a model of a CB network for a set of banks based on existing relationships among and between the banks; and an optimization engine that generates a CB transaction routing scheme for an inputted set of CB transactions within the CB network, wherein the optimization engine provides an overall maximized economic benefit for executing each of the inputted set of CB transactions based on separately collected cost data, risk data and revenue data.

Abstract: A system, method and program product are provided for evaluating clients in a correspondent banking (CB) network with regard to CB product offerings. The disclosed system includes: a network modeling system that creates a model of a CB network, wherein the model is defined by a set of banks and a set of clients based on existing relationships among and between the banks and clients, and wherein the model is further defined with inputted risk data, expected return data, demand data, and cost data; and an evaluation engine that utilizes the model to generate a clientele evaluation for an inputted CB product offering within the CB network, wherein the evaluation engine determines a potential economic benefit of each client with regard to the inputted CB product offering.

Abstract: A processor-implemented method, system and/or computer program product allocates multiple resources from multiple organizations. A series of requests for multiple resources from multiple organizations is received. The multiple resources are required to accomplish a specific task, and each of the multiple resources is assigned a probability of consumption. Probabilities of availability of the multiple resources are then determined and transmitted to the organizations.

Abstract: Embodiments relate to privacy preserving content analysis. A recoverable hash operation is performed on text information to produce hashed text information in a business-to-business system. The business-to-business system includes a business-to-business transaction gateway coupled to a plurality of enterprise computer systems. A non-recoverable hash operation is performed on numerical information to produce hashed numerical information in the business-to-business system. The hashed text information and the hashed numerical information are provided from the business-to-business transaction gateway to an analytics engine to perform encrypted content analysis. The text information and the numerical information are provided from one of the enterprise computer systems as a producer system to another of the enterprise computer systems as a consumer system through the business-to-business transaction gateway.

Abstract: Embodiments relate to analytics driven assessment of transactional risk daily limit (TRDL) exceptions. A transaction that includes a request to make a payment from an account associated with a customer is received and it is determined that processing the payment will result in exceeding a TRDL. Customer data including historical transaction data and customer profile data associated with the customer is accessed by a processor. Economic data from an external data source is accessed via a network from an external data source. A TRDL exception assessment model is applied, by the processor, to the transaction, the customer data, and the economic data to generate an approval recommendation for the request and a confidence level associated with the approval recommendation.

Abstract: Embodiments relate to intraday cash flow optimization. Transactions are accessed on a business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system. The transactions are associated with two or more compartmentalized entities. The transactions are characterizes based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. An intraday receivables prediction engine and an intraday payables prediction engine are applied to the integrated information to produce an estimation of intraday cash flow. The estimation of intraday cash flow is monitored relative to intraday operations optimization conditions. An alert is generated based on determining that at least one of the intraday operations optimization conditions is met.

Abstract: An aspect of product recommendation processes includes classifying customers into groups based on commonly shared, predefined characteristics and common financial transaction activities conducted. For each service offered, the product recommendation processes include estimating a cost of recommendation of the service; and estimating, for each of the customers in a group, a transaction risk of providing the service.

Abstract: An aspect of product pricing includes classifying customers into groups based on shared, predefined characteristics and financial transactions, and identifying services rendered and available but not rendered. For each customer, a risk associated with a service is estimated; availability and prices of the service by third parties are determined; a price for the service set by the entity is compared with the prices set by the third parties; and a demand for the service of the entity is estimated as a function of the availability and prices of the service by the third parties. For each customer, a probability that the customer will purchase the service is estimated based on the demand, and a price for the service that is customized for the customer is calculated, as a function of the risk, the demand, and the probability of purchase, and in view of a target profit and/or target revenue.

Abstract: Embodiments relate to analytics-driven automated reconciliation of financial transactions. External information is correlated with a plurality of financial transaction reconciliation exceptions associated with a sequence of financial transactions over a period of time. A plurality of causal factors is identified from the external information associated with a pattern of the financial transaction reconciliation exceptions. A plurality of more recent financial transactions is monitored for the causal factors. An exception prediction alert is issued based on identifying the causal factors in the more recent financial transactions prior to detecting a new financial transaction reconciliation exception associated with the more recent financial transactions.

Abstract: An aspect of product recommendation processes includes classifying customers into groups based on commonly shared, predefined characteristics and common financial transaction activities conducted. For each service offered, the product recommendation processes include estimating a cost of recommendation of the service; and estimating, for each of the customers in a group, a transaction risk of providing the service.

Abstract: Embodiments relate to analytics driven assessment of transactional risk daily limits (TRDLs). Customer data that includes historical transaction data and customer profile data associated with a customer is accessed by a processor. Economic data from an external data source is accessed via a network. A TRDL assessment model is applied, by a processor, to the customer data and the economic data to generate a TRDL for the customer.

Abstract: An aspect of product pricing includes classifying customers into groups based on shared, predefined characteristics and financial transactions, and identifying services rendered and available but not rendered. For each customer, a risk associated with a service is estimated; availability and prices of the service by third parties are determined; a price for the service set by the entity is compared with the prices set by the third parties; and a demand for the service of the entity is estimated as a function of the availability and prices of the service by the third parties. For each customer, a probability that the customer will purchase the service is estimated based on the demand, and a price for the service that is customized for the customer is calculated, as a function of the risk, the demand, and the probability of purchase, and in view of a target profit and/or target revenue.

Abstract: Embodiments relate to a transactional risk daily limit (TRDL) update alarm. Customer data including historical transaction data and customer profile data is accessed, along with economic data from an external data source. A TRDL alarm analytics model is applied to the customer data and the economic data to predict a number of transactions in a specified time period that are expected to exceed a TRDL. The TRDL alarm analytics model takes into account a payment transaction pattern associated with the customer. A threshold value is compared to the number of transactions in the specified time period that are expected to exceed the TRDL. An increase in the TRDL is requested to be applied at least during the specified time period based on the number of transactions in the specified time period that are expected to exceed the TRDL being greater than the threshold value.