Abstract: A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Abstract: A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Abstract: A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Abstract: A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Abstract: A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Abstract: An exemplary system, method, and computer-accessible medium can include receiving financial information related to a transaction(s) at a first merchant, where the transaction(s) can be based on a financial instrument that can be associated with a second merchant, and where the first merchant can be different than the second merchant, declining the transaction(s), and determining a breach at the second merchant based on the transaction(s). The financial instrument can be a virtual credit card number. The virtual credit card number can be bound to the second merchant. A further transaction(s) can be received at (i) the first merchant or a (ii) third merchant, where the further transaction(s) can be based on (i) the financial instrument or (ii) a further financial instrument that can be associated with the second merchant, and where the third merchant can be different than the second merchant and the first merchant.

Abstract: A behavioral analytics platform may obtain a first data set associated with a first user and a second data set associated with a second user. The behavioral analytics platform may determine a first set of behavioral categories to classify the historical user activity data associated with the first user based on a first set of behavior vectors and determine a second set of behavioral categories to classify the historical user activity data associated with the second user based on a second set of behavior vectors. The behavioral analytics platform may populate one or more user interfaces that are accessible to the first user and/or the second user based on one or more values representing a degree to which the first set of behavioral categories and the second set of behavioral categories correspond to complementary behavioral tendencies, which may be determined using a machine learning technique.

Abstract: A behavioral analytics platform may obtain a first data set associated with a first user and a second data set associated with a second user. The behavioral analytics platform may determine a first set of behavioral categories to classify the historical user activity data associated with the first user based on a first set of behavior vectors and determine a second set of behavioral categories to classify the historical user activity data associated with the second user based on a second set of behavior vectors. The behavioral analytics platform may populate one or more user interfaces that are accessible to the first user and/or the second user based on one or more values representing a degree to which the first set of behavioral categories and the second set of behavioral categories correspond to complementary behavioral tendencies, which may be determined using a machine learning technique.

Abstract: Generally, disclosed is an automatic messaging system for providing a text or other message to a third party that notifies said party when a person has been arrested and is being held in police custody. Also disclosed may be a system employing software that will alert a family member whenever a loved one has been incarcerated. In one embodiment, a user can sign up for a service so that the user's name and information will be entered into an information database and used to monitor hundreds of public police arrest records from various localities wherein, if the user is arrested and incarcerated, then the service will automatically send a notification to a designated contact, such as the user's attorney, family member, or friend.

Abstract: Generally, disclosed is an automatic messaging system for providing a text or other message to a third party that notifies said party when a person has been arrested and is being held in police custody. Also disclosed may be a system employing software that will alert a family member whenever a loved one has been incarcerated. In one embodiment, a user can sign up for a service so that the user's name and information will be entered into an information database and used to monitor hundreds of public police arrest records from various localities wherein, if the user is arrested and incarcerated, then the service will automatically send a notification to a designated contact, such as the user's attorney, family member, or friend.