Toll-free numbers metadata tagging, analysis and reporting

A method comprising:

• providing one or more services for a phone number via a telecommunications services management platform;
• receiving, at the telecommunication services management platform, one or more datasets corresponding at least in part to the one or more services;
• analyzing, at the telecommunication services management platform and based at least in part on a machine learning model, the one or more datasets to identify unusual call activity;
• generating, at the telecommunications services management platform, a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud;
• receiving a one-click activate request from a user, wherein the request includes at least a customer record template reference and an indication of when to active a toll-free number associated with the request;
• searching a responsible organization record to determine a presence of a defined customer template record relating to the user request, wherein the responsible organization is associated with toll-free telecommunications;
• retrieving at least one customer template record, wherein the customer template record is a defined customer template record for the responsible organization;
• and
• activating the user request, wherein the activation includes at least one of activating or reserving the toll-free number.

A method comprising:

• providing one or more services for a phone number via a telecommunications services management platform;
• receiving, at the telecommunication services management platform, one or more datasets corresponding at least in part to the one or more services;
• analyzing, at the telecommunication services management platform and based at least in part on a machine learning model, the one or more datasets to identify unusual call activity;
• generating, at the telecommunications services management platform, a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud;
• creating at least two toll-free call routing tables based on a congestion threshold criterion, wherein a first of the two call routing tables is to be used when toll-free call volumes occurring within a toll-free telecommunications carrier network are below the congestion threshold, and a second of the two call routing tables is to be used when toll-free call volumes occurring within a toll-free telecommunications carrier network are equal to or above the congestion threshold;
• providing the call routing tables to at least one service control point that is associated with the toll-free telecommunications carrier network;
• monitoring toll-free call volumes and durations occurring within a toll-free telecommunications carrier network;
• receiving at least one of a call count datum or call duration datum from the toll-free telecommunications carrier network wherein the call count datum or call duration datum indicates a change in call volumes over the toll-free telecommunications carrier network from below the congestion threshold to above the congestion threshold;
• and
• instructing the service control point to switch from using the first call routing table to the second call routing table.

A system comprising:

• at least one processor operative to:
• provide one or more services for a phone number via a telecommunications services management platform;
• receive, at the telecommunication services management platform, one or more datasets corresponding at least in part to the one or more services;
• analyze, at the telecommunication services management platform and based at least in part on a machine learning model, the one or more datasets to identify unusual call activity;
• generate, at the telecommunications services management platform, a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud;
• create at least two toll-free call routing tables based on a congestion threshold criterion, wherein a first of the two call routing tables is to be used when toll-free call volumes occurring within a toll-free telecommunications carrier network are below the congestion threshold, and a second of the two call routing tables is to be used when toll-free call volumes occurring within a toll-free telecommunications carrier network are equal to or above the congestion threshold;
• provide the call routing tables to at least one service control point that is associated with the toll-free telecommunications carrier network;
• monitor toll-free call volumes and durations occurring within a toll-free telecommunications carrier network;
• receive at least one of a call count datum or call duration datum from the toll-free telecommunications carrier network wherein the call count datum or call duration datum indicates a change in call volumes over the toll-free telecommunications carrier network from below the congestion threshold to above the congestion threshold;
• and
• instruct the service control point to switch from using the first call routing table to the second call routing table.

A method comprising:

• providing one or more services for a phone number via a telecommunications services management platform;
• receiving, at the telecommunication services management platform, one or more datasets corresponding at least in part to the one or more services;
• analyzing, at the telecommunication services management platform and based at least in part on a machine learning model, the one or more datasets to identify unusual call activity;
• generating, at the telecommunications services management platform, a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud;
• receiving data relating to toll-free number call activity from a toll-free telecommunications system, wherein the data includes at least one of call duration or call count data;
• receiving third party data relating to macroeconomic activity;
• modeling at least one of call duration or call count data with the third party data to derive a macroeconomic trend;
• receiving a request from a client device to present the macroeconomic trend;
• and
• presenting a representation of the macroeconomic trend to a user interface on the client device.

A system comprising:

• at least one processor operative to:
• provide one or more services for a phone number via a telecommunications services management platform;
• receive, at the telecommunication services management platform, one or more datasets corresponding at least in part to the one or more services;
• analyze, at the telecommunication services management platform and based at least in part on a machine learning model, the one or more datasets to identify unusual call activity;
• generate, at the telecommunications services management platform, a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud;
• receive data relating to toll-free number call activity from a toll-free telecommunications system, wherein the data includes at least one of call duration or call count data;
• receive third party data relating to macroeconomic activity;
• model at least one of call duration or call count data with the third party data to derive a macroeconomic trend;
• receive a request from a client device to present the macroeconomic trend;
• and
• present a representation of the macroeconomic trend to a user interface on the client device.