Abstract: The present disclosure provides a computer-implemented method, which includes receiving a dispute information pertaining to a billing dispute by a processor, from a device associated with a customer. The method includes computing, by the processor, a score based on the dispute information, for determining validity of the billing dispute. The billing dispute is categorized based on the score computed for the dispute information. One or more actions are subsequently presented to one or more parties associated with the dispute information for resolving the billing dispute. The processor is also configured to prioritize the one or more actions based on the score computed. The present disclosure also provides a system capable of implementing the aforesaid method for prioritizing and resolving the billing disputes in an automated manner.

Abstract: Methods and systems for assessing performance and risk in financing supply chain are disclosed. The method includes accessing a behavioral data of an entity. The behavioral data comprises at least one of a primary data and a secondary data. The primary data is associated with one or more assets of the entity and comprises historical transaction data of the entity with financial institutions. The secondary data is sourced from an external system and comprises at least a credit information and a business transaction information of the entity. The method also includes generating a unified model for the entity based on the behavioral data of the entity. The method includes predicting a plurality of risk metrics based on the unified model using one or more machine learning models, and determining a credit rating of the entity based on the plurality of risk metrics and a confidence measure.

Abstract: Embodiments provide methods and systems for generating a plain-text explanation for a prediction score associated with a record. An explanation generation system is configured to receive the record from a user. A prediction score is calculated using an ML model and a plurality of feature variables that are contributing to the prediction score are determined by the system. The plurality of feature variables are rank-ordered by the system based on their corresponding contribution to the prediction score. Further, correlated features are determined from among the plurality of feature variables and are grouped into one or more groups of correlated feature variables. At least one feature variable from each of the one or more groups is selected to determine a list of feature variables. The list of feature variables is passed to a sentence creation module that generates a plain-text explanation. The generated plain-text explanation is displayed to the user.

Abstract: The present disclosure provides a computer-implemented method, which includes receiving a dispute information pertaining to a billing dispute by a processor, from a device associated with a customer. The method includes computing, by the processor, a score based on the dispute information, for determining validity of the billing dispute. The billing dispute is categorized based on the score computed for the dispute information. One or more actions are subsequently presented to one or more parties associated with the dispute information for resolving the billing dispute. The processor is also configured to prioritize the one or more actions based on the score computed. The present disclosure also provides a system capable of implementing the aforesaid method for prioritizing and resolving the billing disputes in an automated manner.

Abstract: Methods and systems for assessing performance and risk in financing supply chain are disclosed. The method includes accessing a behavioral data of an entity. The behavioral data comprises at least one of a primary data and a secondary data. The primary data is associated with one or more assets of the entity and comprises historical transaction data of the entity with financial institutions. The secondary data is sourced from an external system and comprises at least a credit information and a business transaction information of the entity. The method also includes generating a unified model for the entity based on the behavioral data of the entity. The method includes predicting a plurality of risk metrics based on the unified model using one or more machine learning models, and determining a credit rating of the entity based on the plurality of risk metrics and a confidence measure.