Abstract: A system and a method for optimizing supply of rental vehicles, comprising receiving customer data associated with a plurality of customers from a plurality of sources; classifying the customers into a plurality of segments, based on said data, each segment being indicative of vehicle rental preferences of customers in the segment; determining whether any customers have already opted for a vehicle rental; determining the destination and source location of the customer and predicting likely vehicle demand for different vehicles based on said classification and said determination.

Abstract: The present disclosure generally relates to an electronic system and method for management of cash in automated teller machines (ATMs). The system comprises a server operative within a financial network, the server including a processor and a memory configured to store computer-readable instructions. When the instructions are executed, the processor performs steps of the method, comprising: obtaining location data and historical data on cash withdrawals of each ATM of the financial network; identifying a group of ATMs within a predefined zone based on the location data; calculating an expected combined cash withdrawal amount of the group of ATMs for a subsequent time period based on the cash withdrawals historical data; and computing cash replenishment details for a subset of the group of ATMs based on the expected combined cash withdrawal amount, wherein the cash replenishment details aims to maintain a positive cash balance in the subset of ATMs during the subsequent time period.

Abstract: For each of multiple customers, a computer system constructs a customer preference model using transactional level data relating to purchases the customers have made using respective payment cards. The computer system has access to a database with merchant data, including profiles of merchants. Upon the customer contacting the computer system in respect of a specific product, the computer system (or an application running on a communication device of the customer which is able to access the customer's preference model and the database of merchant data) uses the customer preference model to match the customer to merchants who offer the product and who have profile matching the customer's preference model. The customer may provide details of his/her payment card identify the customer to the computer system.

Abstract: The present disclosure generally relates to an electronic system and method for management of cash in automated teller machines (ATMs). The system comprises a server operative within a financial network, the server including a processor and a memory configured to store computer-readable instructions. When the instructions are executed, the processor performs steps of the method, comprising: obtaining location data and historical data on cash withdrawals of each ATM of the financial network; identifying a group of ATMs within a predefined zone based on the location data; calculating an expected combined cash withdrawal amount of the group of ATMs for a subsequent time period based on the cash withdrawals historical data; and computing cash replenishment details for a subset of the group of ATMs based on the expected combined cash withdrawal amount, wherein the cash replenishment details aims to maintain a positive cash balance in the subset of ATMs during the subsequent time period.

Abstract: A system and a method for optimizing supply of rental vehicles, comprising receiving customer data associated with a plurality of customers from a plurality of sources; classifying the customers into a plurality of segments, based on said data, each segment being indicative of vehicle rental preferences of customers in the segment; determining whether any customers have already opted for a vehicle rental; determining the destination and source location of the customer and predicting likely vehicle demand for different vehicles based on said classification and said determination.

Abstract: For each of multiple customers, a computer system constructs a customer preference model using transactional level data relating to purchases the customers have made using respective payment cards. The computer system has access to a database with merchant data, including profiles of merchants. Upon the customer contacting the computer system in respect of a specific product, the computer system (or an application running on a communication device of the customer which is able to access the customer's preference model and the database of merchant data) uses the customer preference model to match the customer to merchants who offer the product and who have profile matching the customer's preference model. The customer may provide details of his/her payment card identify the customer to the computer system.

Abstract: A system, method, and computer readable storage medium configured to the process, analyze, and model of large amounts of data resulting in improved functionality over a generic computer.

Abstract: A method for generating a precedent risk-mitigation schedule for a customer segment is provided. The method includes obtaining financial data of a first plurality of at-risk entities defined by a plurality of parameters, and determining a customer segment including a second plurality of at-risk entities having at least one common parameter. The method further includes identifying one or more constraints for a respective one or more parameters defining each at-risk entity in the second plurality of at-risk entities, and using a schedule generator to generate an entity profile for each at-risk entity in the second plurality of at-risk entities, further generate a precedent entity profile based on the entity profile for each at-risk entity, and produce a precedent risk-mitigation schedule for the precedent entity profile. The method also includes identifying a further at-risk entity from the customer segment, and applying the precedent risk-mitigation schedule to the further at-risk entity.

Abstract: A method for determining exceptions in a risk-mitigation schedule of an at-risk entity is provided. The method includes determining a plurality of parameters defining the at-risk entity, each parameter having a value and a value-type. The method also includes identifying a constraint for each parameter, and using a schedule generator to generate an entity profile using the constraints, determine, from at least one precedent profile, a best fit precedent profile based on a minimum deviation of the best fit precedent profile from the entity profile, produce a precedent risk-mitigation schedule for the best fit precedent profile, and determine at least one disparity between the precedent risk-mitigation schedule and the risk-mitigation schedule of the at-risk entity. The method further includes displaying an exceptions schedule to the at-risk entity that includes the at least one disparity.

Abstract: A computer-implemented method is proposed for providing a personalized customer service from a physical provider of goods or services. The method comprises: automatically recognizing when a customer enters a physical provider of goods or services through use of an electronic signature; obtaining data on the recognized customer, the data comprising the customer's historical purchasing data; and informing provider staff of one or more of: the customer data, deals, discounts, recommendations or rewards to offer the customer based on the historical purchasing data, such that the staff can provide a personalized service to the customer based on the data provided.