Abstract: Provided techniques manage and predict future events. For example, in a payment implementation, a supplier, at any given point in time, has multiple customer debtors that may owe payments (e.g., have outstanding invoices). Utilizing historical attributes for a given customer debtor payment predictions may be determined. By analyzing outstanding debts associated with this debtor customer an amount owed may be calculated and a predicted payment (e.g., a payment that has not yet been indicated by that debtor customer) created. Events may be provided to a second system to correlate predictions across multiple debtor collectors. Correlated information may be used to predict cash flow needs of an organization. Alternatively, optimization of help desk systems may be provided based on predictions from analysis of multiple events in an Event-driven feed back system. Provided techniques may be generalized to other applications as well.

Abstract: Provided techniques manage and predict future events. For example, in a payment implementation, a supplier, at any given point in time, has multiple customer debtors that may owe payments (e.g., have outstanding invoices). Utilizing historical attributes for a given customer debtor payment predictions may be determined. By analyzing outstanding debts associated with this debtor customer an amount owed may be calculated and a predicted payment (e.g., a payment that has not yet been indicated by that debtor customer) created. Events may be provided to a second system to correlate predictions across multiple debtor collectors. Correlated information may be used to predict cash flow needs of an organization. Alternatively, optimization of help desk systems may be provided based on predictions from analysis of multiple events in an Event-driven feed back system. Provided techniques may be generalized to other applications as well.

Abstract: A system generates trade deduction settlement rules and associated confidence scores independent of buyer specifications. A machine learning equipped rewards based method performed by the system analyzes historically matched deductions and promotions to understand patterns. Penalties are applied to outdated rules, and recent trends are promoted through rewards. All available deduction-promotion combinations may be analyzed in batches for a given time period at each pair level within an artificial intelligence model of the method. A rules selector selects the most recurring patterns along those combinations based upon definable thresholds. The system finds hidden patterns to provide suggestions for trade deduction settlement. The system further captures the rules and evolves the rules over time.

Abstract: A system generates trade deduction settlement rules and associated confidence scores independent of buyer specifications. A machine learning equipped rewards based method performed by the system analyzes historically matched deductions and promotions to understand patterns. Penalties are applied to outdated rules, and recent trends are promoted through rewards. All available deduction-promotion combinations may be analyzed in batches for a given time period at each pair level within an artificial intelligence model of the method. A rules selector selects the most recurring patterns along those combinations based upon definable thresholds. The system finds hidden patterns to provide suggestions for trade deduction settlement. The system further captures the rules and evolves the rules over time.

Abstract: A computing device performs a method for predictive resource request hold and proactive hold resolution. The method includes: predicting a request for a resource from a requestor; predicting a hold on fulfilling the request; and determining a preventative action to minimize actualization of the hold on fulfilling the request. Predicting the request for the resource and predicting the hold can be performed using artificial intelligence. The preventative action can include temporarily increasing a credit limit for the requestor. Where the hold is actualized, the method can further include predicting a likelihood of the hold being released and determining whether to release the hold based on whether the likelihood of the hold being released exceeds a release threshold.

Abstract: Provided techniques manage and predict future events. For example, in a payment implementation, a supplier, at any given point in time, has multiple customer debtors that may owe payments (e.g., have outstanding invoices). Utilizing historical attributes for a given customer debtor payment predictions may be determined. By analyzing outstanding debts associated with this debtor customer an amount owed may be calculated and a predicted payment (e.g., a payment that has not yet been indicated by that debtor customer) created. Events may be provided to a second system to correlate predictions across multiple debtor collectors. Correlated information may be used to predict cash flow needs of an organization. Alternatively, optimization of help desk systems may be provided based on predictions from analysis of multiple events in an Event-driven feed back system. Provided techniques may be generalized to other applications as well.

Abstract: Systems are provided to utilize machine learning to identify abnormal event resolutions and provide guidance for resolution. For example, in a two-way event system, a normal response will typically close the loop on an initially generated event. However, there are cases where processing of the event uncovers contingent response strategies. In an accounting implementation, machine learning techniques are used to identify the potential of a deduction to be invalid. Machine learning algorithms are trained based on historical deductions and their resolution attributes. Models may further be used to predict whether a deduction is valid or invalid. Contingencies addressed include shortages, pricing adjustments, promotional activity, and other types of deductions that may occur in a provider, supplier, and consumer account resolution system. Automation allows focus on invalid deductions and may automatically close non-cost effective events as having been resolved without further inquiry or research.