Oracle International Corporation

Keshava Mangipudi

Bhupinder Sondhi

Amira A Morcos

Tripti Nair

Venugopal Bheemreddy

Jeremias Romana

Gerhard Schmidt

Deepa Pai Saoji

Babou Srinivasan

Balaji Dasarath

Madhavi Makkapati

Christopher Bharath

Techniques are disclosed for using machine-learning processing for generating resource-allocation specifications. A first data set may be received from a first data source. The first data set can include a first resource request and a first timestamp associated with entities. A second data set can be received from a second data source that includes communication data and allocation data associated with the entities. Target characteristics may be defined for training instances. The training instances can be used to train a machine-learning model using the first data set and the second data set. A third data set may be accessed and used to generate a user session within which, the trained machine-learning model may execute to generate a resource-allocation specification. The resource-allocation specification including a communication schedule. One or more communications compliant with the communication schedule may be output to an entity.

Machine-learning processing at native-location storage system to generate collections action plan

A method, system, and computer program product for enterprise analytics. The method commences by requesting and receiving bank statement information from a bank computer where the bank statement information comprises at least a then current balance, then accessing a transaction repository to retrieve a transaction pipeline comprising deposit transactions and/or withdrawal transactions to be applied at future times to the then current balance. The method continues by forecasting a balance by applying at least a portion of the transaction pipeline to the then current balance. The resulting forecasted balance and reports are displayed for review by a user. The displays can include desktop or mobile displays or both. Transactions that involve multiple currencies can be converted into a selected currency of choice, and the balances and transactions are displayed in the denomination of the selected currency.

Cash position forecasting with real-time analytics

Mechanisms and approaches for a collaborative financial close portal are provided. A client receives display data, which when rendered by the client, depicts a collaborative financial close portal that supports one or more workspaces. Each of the one or more workspaces may be used by a different set of users to perform activities related to a financial close process. The one or more workspaces are arranged in a hierarchical relationship. In response to receiving identification data that identifies a user of the client, additional display data is sent to the client, which when rendered, depicts a particular workspace for which the user is granted access. The depiction of the particular workspace includes depicting status information about financial close activities associated with one or more workspaces lower in the hierarchal relationship than the particular workspace.

Collaborative financial close portal

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Pleasanton, CA, United States of America

Keshava Mangipudi