Method and apparatus to profile account credential sharing
Aspects of the subject disclosure may include, for example, a system or method that determines, based on consumption data and location information, groups of access portals, where the consumption data and the location information correspond to a time period in which the access portals are used to access a service using an account. The groups can be adjusted according to an intended benefit unit for the service and a sharing structure can be determined including benefit unit partitions for the account based on the adjusting. Factors can be determined, based on the benefit unit partitions, that are indicative of account sharing, as well as weights for the factors by applying a learning model to the consumption data. An intervention strategy can be selected from a group of intervention strategies according to the factors and the weights for the factors. Other embodiments are disclosed.
1. A method, comprising:
monitoring over a time period for an account of a service, by a processing system including a processor, consumption data for access portals and corresponding location information for each of the access portals that are used to access the service using the account;
determining, by the processing system and based at least on the location information, groups of the access portals according to the monitoring;
adjusting, by the processing system, the groups according to an intended benefit unit for the service;
determining, by the processing system, a sharing structure including benefit unit partitions for the account based on the adjusting;
determining, by the processing system and based on the benefit unit partitions, factors indicative of account sharing by applying a learning model to the consumption data;
determining, by the processing system using the learning model, weights for the factors;
computing, by the processing system, a weighted sum of the factors;
determining, by the processing system, a severity category for the account according to the weighted sum of the factors; and
selecting, by the processing system, an intervention strategy from a group of intervention strategies according to the severity category.
2. The method of claim 1 , comprising:
repeating, by the processing system for a group of accounts, the monitoring, the determining the groups, the adjusting the groups, the determining the sharing structure, the determining the factors, and the determining the weights for the factors; and
clustering accounts of the group of accounts according to one or more of the factors by using a clustering algorithm, applying filtering rules to all or a subset of the factors, or a combination thereof.
3. The method of claim 2 , comprising determining filtering thresholds for one or more of the factors.
4. The method of claim 2 , comprising determining targeted offers for the group of accounts.
5. The method of claim 1 , wherein the determining the groups of the access portals is further based on other data including at least one of the consumption data, social media information, contacts information, demographic information, content popularity, or a combination thereof, and wherein the intended benefit unit for the service is one of an individual, a household, a business entity, a school, or a community.
6. The method of claim 1 , wherein the service comprises an entertainment service, an instructional service, a hospitality service, an exercise service, a utility or software service, an e-commerce service, or a business privilege, and wherein the adjusting the groups is based on a type of device implementing the particular one of the access portals.
7. The method of claim 1 , wherein the adjusting the groups is based on a characteristic of a location of the location information.
8. A device, comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
accessing consumption data for access portals and corresponding location information for each of the access portals that are used to access a service using an account over a time period;
determining, based at least on the consumption data and the location information, groups of the access portals;
adjusting the groups according to an intended benefit unit for the service;
determining a sharing structure including benefit unit partitions for the account based on the adjusting;
determining, based on the benefit unit partitions, factors indicative of account sharing and weights for the factors by applying a learning model to the consumption data; and
selecting an intervention strategy from a group of intervention strategies according to the factors and the weights for the factors.
9. The device of claim 8 , wherein the determining the groups of the access portals is further based on other data including at least one of social media information, contacts information, demographic information, content popularity, or a combination thereof, and wherein the operations further comprise:
determining a severity category for the account according to a weighted sum of the factors.
10. The device of claim 8 , wherein the operations further comprise:
repeating, for a group of accounts, the accessing, the determining the groups, the adjusting the groups, the determining the sharing structure, the determining the factors, and the determining the weights for the factors; and
clustering accounts of the group of accounts according to one or more of the factors by using a clustering algorithm, applying filtering rules to all or a subset of the factors, or a combination thereof.
11. The device of claim 8 , wherein the operations further comprise determining filtering thresholds for one or more of the factors.
12. The device of claim 8 , wherein the operations further comprise determining targeted offers for the group of accounts.
13. The device of claim 8 , wherein the intended benefit unit for the service is one of an individual, a household, a business entity, a school, or a community.
14. The device of claim 8 , wherein the adjusting the groups is based on a type of device implementing the particular one of the access portals.
15. The device of claim 8 , wherein the adjusting the groups is based on a characteristic of a location of the location information.
16. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
determining, based at least on consumption data and location information, groups of access portals, wherein the consumption data and the location information correspond to a time period in which the access portals are used to access a service using an account;
adjusting the groups according to an intended benefit unit for the service;
determining a sharing structure including benefit unit partitions for the account based on the adjusting;
determining, based on the benefit unit partitions, factors indicative of account sharing and weights for the factors by applying a learning model to the consumption data; and
selecting an intervention strategy from a group of intervention strategies according to the factors and the weights for the factors.
17. The non-transitory machine-readable medium of claim 16 , wherein the operations further comprise determining a severity category for the account according to a weighted sum of the factors, and wherein one or more of the determining the groups of access portals, the adjusting the groups, the determining the sharing structure or the determining the factors is based on a timestamp, connection duration, session information, IP address, GPS location, cellular location, physical location, device identifier, cellular number, account or sub-account number, benefit to be consumed, historical record of consumption, or a combination thereof.
18. The non-transitory machine-readable medium of claim 16 , wherein the operations further comprise:
repeating, for a group of accounts, the determining the groups, the adjusting the groups, the determining the sharing structure, the determining the factors, and the determining the weights for the factors; and
clustering accounts of the group of accounts according to one or more of the factors by using a clustering algorithm, applying filtering rules to all or a subset of the factors, or a combination thereof.
19. The non-transitory machine-readable medium of claim 16 , wherein the determining the groups of the access portals is further based on other data including at least one of social media information, contacts information, demographic information, content popularity, or a combination thereof, and wherein the operations further comprise determining filtering thresholds for one or more of the factors.
20. The non-transitory machine-readable medium of claim 16 , wherein the intended benefit unit for the service is one of an individual, a household, a business entity, a school, or a community.