Oracle Financial Services Software Limited

Mridul Kumar Nath

Shubham Negi

Rupa Satyabodha Kolhar

Abraham Ivan

Prajwal Patil

Shital Reprendra Singh Chauhan

Abhishek Anand

Vipesh Ambala Parambath

Anshul Kumar Jain

Shweta Shree

Machine learning techniques are disclosed for predicting a task event such as a service completion event based on a predefined workflow. In one aspect a method includes obtaining initial data for a service request (e.g., an account application), enriching the initial data with data from one or more repositories of an enterprise executing the service request, generating a data structure comprising independent variables extracted from the enriched data, receiving a request for a prediction of a completion time for the service request (e.g., an account opening event) at a first time during processing of the service request in accordance with each workflow, in response to receiving the request for the prediction, inputting the data structure into a machine-learning regression model, predicting, using the machine-learning regression model, a completion time for the service request, and providing the completion time for the service request.

Machine learning techniques to predict task event

Method includes obtaining sample records from dataset associated with user and including records associated with identifiers customers of user; executing first clustering using sample records, to obtain first set of clusters for first identifiers associated with sample records, first clustering using features associated with first identifiers; providing visualization of first set of clusters; determining whether user input for optimizing first set of clusters provided in visualization is received; when user input for optimizing first set of clusters is not received, determining first information related to first set of clusters as final result information; when user input for optimizing first set of clusters is received: executing second clustering using sample records, to obtain second set of clusters for first identifiers, second clustering using features associated with first identifiers, and determining second information related to second set of clusters as final result information; and clustering entire dataset using final result information.

Data segmentation using clustering

Machine learning techniques are disclosed for rebuilding transactions to predict cash position. In one aspect a method includes obtaining data for an original transaction, classifying the original transaction into a class of multiple classes based on the data, predicting first tranche delay days for the original transaction based on the class and the data, predicting a tranche count for the original transaction based on the class and the data, predicting a tranche interval for the original transaction based on the class and the data; and rebuilding the original transaction as one or more future transactions based on the class, the first tranche delay days, the tranche count, and tranche interval. Each of the one or more future transactions comprise an updated amount of the original transaction, an updated date upon which the original transaction is anticipated, or both.

Machine learning techniques for rebuilding transactions to predict cash position

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Bengaluru, India

Mridul Kumar Nath