Abstract: Provided is a system for developing a geographic agnostic machine learning model. The system may select transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, normalize the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, generate a machine learning model using the training data, and determine a classification of an input using the machine learning model. A method and computer program product are also disclosed.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.

Abstract: A method, system, and computer program product for determining customer migration: obtain first feature data for a plurality of users associated with a first time period; segment the plurality of users into a plurality of groups based on the first feature data; identify a center point of each group based on the feature data; obtain second feature data for one or more users of the plurality of users associated with a second time period; determine a distance between the one or more users and each center point of each group based on the second feature data; and assign the one or more users to a group of the plurality of groups based on the distance between the one or more users and each center point of each group.

Abstract: A method of segmenting a plurality of users in a first region based on predicted activity external to the first region. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for linking datasets using at least two keys, including matching a second key of a first dataset with a second key of a second dataset to identify a plurality of matching records, linking the plurality of matching records from the first dataset and the second dataset, matching the first key of the first dataset with the first key of the second dataset to identify a second plurality of matching records, and linking the second plurality of matching records from the first dataset and the second dataset.

Abstract: A method, system, and computer program product for determining customer migration obtain first feature data for a plurality of users associated with a first time period; segment the plurality of users into a plurality of groups based on the first feature data; identify a center point of each group based on the feature data; obtain second feature data for one or more users of the plurality of users associated with a second time period; determine a distance between the one or more users and each center point of each group based on the second feature data; and assign the one or more users to a group of the plurality of groups based on the distance between the one or more users and each center point of each group.

Abstract: A method of segmenting a plurality of users in a first region based on predicted activity external to the first region. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for linking datasets using at least two keys, including matching a second key of a first dataset with a second key of a second dataset to identify a plurality of matching records, linking the plurality of matching records from the first dataset and the second dataset, matching the first key of the first dataset with the first key of the second dataset to identify a second plurality of matching records, and linking the second plurality of matching records from the first dataset and the second dataset.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.