Abstract: Methods and systems for automatically identifying umbrella sub-funds from within an umbrella investment fund are provided. The method includes: receiving a first data set that includes entity-specific records; retrieving, for each respective entity-specific record, a corresponding set of entity-specific reference data; extracting, from each respective set of reference data, a corresponding umbrella name; determining, based on the corresponding umbrella name, whether the respective entity is associated with a particular sub-fund; and associating the respective entity with other entities that have previously been determined as being associated with the particular sub-fund. The determination may be made by applying an artificial intelligence (AI) algorithm that uses a machine learning technique to determine a probability with respect to whether the respective entity is associated with the particular sub-fund.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method and a system for generating an interpretable embedding that corresponds to a sequence of events is provided. The method includes: receiving information that corresponds to a sequence of events that respectively correspond to interactions between a customer and an organization; determining, for each respective event, a respective product associated with the organization and a respective channel via which the event has occurred; assigning a respective sentiment to each event; computing a respective weight for each event; aggregating the computed weights with respect to the products and the channels; and using the aggregated weights to generate the interpretable embedding for the customer. The interpretable embedding is then usable for generating targeted offers to the customer, handling complaints, and preventing subsequent complaints.

Abstract: A method and a system for using an online learning framework for mixture of multivariate Hawkes processes to model sequences of events are provided. The method includes: receiving data that corresponds to a group of event sequences; generating a mixture of multivariate Hawkes processes model based on the group of event sequences; and adjusting the model by applying an online learning algorithm to the generated model. The online learning algorithm includes an E-step that corresponds to updating a set of responsibilities that relates to the group of event sequences and an M-step that corresponds to updating Hawkes processes parameters that relate to the group of event sequences.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.