Abstract: Disclosed is an apparatus for matching a payment received from a remitter for two or more invoices generated by a beneficiary against invoices of two or more customers associated with the remitter. The apparatus includes a storage unit for storing a plurality of computer program instructions, a graphical user interface for displaying a graphical output, and a processing unit for processing the plurality of computer program instructions. The plurality of computer program instructions includes a payment receive module to receive a payment from the remitter, a total amount module to display the total amount received from the remitter, a customer account module to display a list of invoices generated by the beneficiary against the customers associated with the remitter, and a split payment module to split the total amount into multiple split amounts. Each split amount matches the amount on at least one customer's invoice, and the total amount matches the sum of amounts on two or more customer's invoices.

Abstract: A novel opposing polarity machine learning device and method is described, where two machine learning models are generated, one for each polarity (accepting and rejecting, for example). The device may include memory, connected to circuitry, the memory including historical records of opposing polarities, an input record received from an input device, and instructions for the circuitry.

Abstract: This document describes a non-transitory computer readable media programmed to enrich an entered record submitted to be matched with a dataset record stored on a data storage device. The enrichment is done by supplementing data in the entered record with customer data from a dataset. The media is further programmed to search through a plurality of dataset records in the dataset for the entered record. The search is programmed to first determine if the entered record unambiguously matches one of the dataset records or if the entered record unambiguously does not match one of the dataset records. If the entered record does not unambiguously match one of the dataset records, score match characteristics using a Fellegi-Sunter algorithm, save the score as a highest score if the score is above the highest score less a threshold, and save a location of one of the dataset records as a matching record if the score is above a previous highest score.

Abstract: A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Abstract: A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Abstract: A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Abstract: A method for using machine learning techniques to analyze past decisions (made be administrators concerning account opening requests) and to recommend whether an account opening request should be allowed or denied.