Abstract: A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.

Abstract: An enhanced customer relationship management (CRM) system is provided. The enhanced CRM system performs activities automatically and with the assistance of artificial intelligence and machine learning based on historical information. The enhanced CRM system provides: a) scheduling assistance prior to a customer contact, b) assistance during a call to direct call focus and achieve a personal connection between a customer facing user of the CRM system and a target customer contact, and c) automated assistance to complete a contact and transition to a next target customer contact. Achievement goals for a customer facing user may be presented and monitored with respect to a goal achievement period. Schedules may be dynamically adjusted across multiple CRM system users and with respect to overall organizational goals to enhance achievement of goals.

Abstract: Novel tools and techniques are provided for implementing identification and removal of noise from documents, and, more particularly, to methods, systems, and apparatuses for implementing identification and removal of noise from financial documents using one or more machine learning algorithms. In various embodiments, computing system might receive a document. The computing system might detect, using one or more machine learning algorithms, that noise exists in the document. Based on the detection that noise exists in the document, the computing system might remove the noise from the document. Once the noise is removed from the document, the computing system might generate a copy of the document with the noise removed while retaining important or useful information contained in the document.

Abstract: A system to process virtual credit card (VCC) payments automatically is provided. Electronic delivery of VCC payment information may be transferred as electronic data representing a VCC payment to the seller (e.g., an email). Data exchange methods for VCC payments include application programming interfaces (APIs), email, files, etc. Secure processing is provided via an environment that meets rigorous security standards (e.g., Payment Card Industry (PCI) data security standards). Credentials of the sending entity establish the context that allows the system to associate the data received with the correct buyer and seller. Authentication may be provided within the context of the data transferred. Authentication may also use the sender's email address to validate the sender using a digital signature of the email contents. The system may combine digital signature validation of an email with a validation of a sending entity using the DomainKeys Identified Mail (DKIM) email authentication protocol.

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 to process virtual credit card (VCC) payments automatically is provided. Electronic delivery of VCC payment information may be transferred as electronic data representing a VCC payment to the seller (e.g., an email). Data exchange methods for VCC payments include application programming interfaces (APIs), email, files, etc. Secure processing is provided via an environment that meets rigorous security standards (e.g., Payment Card Industry (PCI) data security standards). Credentials of the sending entity establish the context that allows the system to associate the data received with the correct buyer and seller. Authentication may be provided within the context of the data transferred. Authentication may also use the sender's email address to validate the sender using a digital signature of the email contents. The system may combine digital signature validation of an email with a validation of a sending entity using the DomainKeys Identified Mail (DKIM) email authentication protocol.

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: An enhanced customer relationship management (CRM) system is provided. The enhanced CRM system performs activities automatically and with the assistance of artificial intelligence and machine learning based on historical information. The enhanced CRM system provides: a) scheduling assistance prior to a customer contact, b) assistance during a call to direct call focus and achieve a personal connection between a customer facing user of the CRM system and a target customer contact, and c) automated assistance to complete a contact and transition to a next target customer contact. Achievement goals for a customer facing user may be presented and monitored with respect to a goal achievement period. Schedules may be dynamically adjusted across multiple CRM system users and with respect to overall organizational goals to enhance achievement of goals.