Abstract: A method, system and computer-usable medium are disclosed for understanding and discovering relationships within infrastructure systems via an infrastructure management system. In certain embodiments, the infrastructure systems comprise water and/or transportation systems. Such an infrastructure management system enables accidents to be prevented, traffic to flow normally, water to be conserved, cleaned and delivered safely.

Abstract: A method, system and computer-usable medium are disclosed for understanding and discovering relationships within infrastructure systems via an infrastructure management system. In certain embodiments, the infrastructure systems comprise water and/or transportation systems. Such an infrastructure management system enables accidents to be prevented, traffic to flow normally, water to be conserved, cleaned and delivered safely.

Abstract: A method and system optimizing source selection of an online order with the lowest fulfillment cost by considering multiple types of parameters, including shipping costs, backlog costs and markdown savings of the order. The method includes obtaining an order from the order retrieval subsystem of the OMS, selecting the candidate sources, and retrieving data from retailers or shipping companies of each selected candidate sources. The system then calculates the costs and savings parameters of the candidate sources from the retrieved data. The system identifies all possible candidate sourcing selections of the order and calculates the total fulfillment cost of each sourcing selection of the order by adding the shipping costs with the backlog costs, and subtracting the markdown savings of all candidate sources in each sourcing selection. The system identifies the optimized sourcing selection of the order with the lowest fulfillment cost and renders the selection to the OMS.

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.

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 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 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: 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 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: 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.

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 customer selection processes includes classifying, by a computer processor, customers of an entity into groups based on commonly shared, predefined characteristics among the customers. For each of the groups: services rendered for corresponding customers are identified; for each of the services rendered, a risk relationship and a reward relationship between each of the corresponding customers and the service is determined; and for each of the services rendered, a score that defines a combination of the risk relationship and the reward relationship is calculated. For each of the services rendered by the entity, the corresponding score is applied to a candidate customer having a set of characteristics matching the characteristics of one of the groups, and the service is offered to the candidate customer as a function of the score.

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 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: 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: An aspect of customer selection processes includes classifying, by a computer processor, customers of an entity into groups based on commonly shared, predefined characteristics among the customers. For each of the groups: services rendered for corresponding customers are identified; for each of the services rendered, a risk relationship and a reward relationship between each of the corresponding customers and the service is determined; and for each of the services rendered, a score that defines a combination of the risk relationship and the reward relationship is calculated. For each of the services rendered by the entity, the corresponding score is applied to a candidate customer having a set of characteristics matching the characteristics of one of the groups, and the service is offered to the candidate customer as a function of the score.

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.