ORACLE FINANCIAL SERVICES SOFTWARE LIMITED

Disclosed herein is a training and deployment framework for a machine learning based recommendation system. In one aspect, a computer-implement method is provided for synthesizing training and testing data and using the training and testing data to generate a machine learning model. The computer-implement method includes deriving product ratings for products, the product ratings being stored in a user product score table in association with identifiers for users and the products, sampling the user product score table to generate a training and testing data set, training, using the training data set, machine learning models for a task of predicting product ratings, evaluating, using the testing data set, performance of the machine learning models, selecting one of the machine learning models for production use in predicting product ratings based on the evaluating, and predicting, using the selected machine learning model, new product ratings for the products.

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 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.

Disclosed herein is a training and deployment framework for a machine learning based recommendation system. In one aspect, a computer-implement method is provided for synthesizing training and testing data and using the training and testing data to generate a machine learning model. The computer-implement method includes deriving product ratings for products, the product ratings being stored in a user product score table in association with identifiers for users and the products, sampling the user product score table to generate a training and testing data set, training, using the training data set, machine learning models for a task of predicting product ratings, evaluating, using the testing data set, performance of the machine learning models, selecting one of the machine learning models for production use in predicting product ratings based on the evaluating, and predicting, using the selected machine learning model, new product ratings for the products.

According to an aspect, a system performs a first machine learning (ML) technique and a second ML technique on a large set of data items. The first ML technique identifies a primary cluster containing data items classified as non-anomalous, with the rest of the data items classified as anomalous. The second ML technique identifies a set of secondary clusters. Upon receiving feedback on the classification of at least some of the data items, the system maintains a feedback data associating the feedback with each secondary cluster. Upon receiving an indication to find a classification of a data item, the system identifies as a result, whether the data item is non-anomalous or anomalous based on whether the data item belongs to the primary cluster or not. The system also determines based on a secondary cluster to which the data item belongs and the feedback associated with the secondary cluster, whether the result is to be modified. If the result is to be modified, the system reverses the result (from anomalous to non-anomalous and vice versa). The system then provides the result as the classification for the data item.

A method includes estimating a number of threads to execute batch job within maximum completion time by executing a thread estimation algorithm. Executing the thread estimation algorithm includes: determining, using an ML model, a collection of one or more combinations, each including an estimated number of threads and an actual completion time for processing the batch job using the estimated number of threads, the actual completion time being less than or equal to the maximum completion time, and saving the collection including the one or more combinations in a list of threads and completion times. The list of threads and completion times is used to select, based on a list of cloud virtual machines available for use, an optimal virtual machine or an optimal virtual machine combination, the optimal virtual machine or the optimal virtual machine combination being capable of executing the batch job within the maximum completion time.

Systems, methods, and other embodiments associated with decision tree machine learning native to a graph database are described. In one embodiment, a method includes receiving an objective property value of a training graph for a graph-native decision tree to be trained to predict; determining feasible ranges of values from the training graph; automatically generating a graph query language grammar for graph-native decision trees based on the feasible ranges; progressively evolving output decision trees in a graph query language from the graph query language grammar to generate a final decision tree that accurately predicts the objective property value; and storing the final decision tree for subsequent execution against a target graph.

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.

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.

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.