Dynamic anomaly detection and response to provisioning errors for telecommunications networks

A system for detecting and responding to anomalies in network provisioning comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• receive network provisioning data from a plurality of service providers each supported by a wireless telecommunications network,
• wherein the network provisioning data includes service requests and/or provisioning status;
• segment the network provisioning data per service provider and based on at least one of the service requests and/or provisioning status;
• process by the network provisioning system, the network provisioning data using an anomaly detection model,
• wherein the anomaly detection model identifies one or more anomalies in provisioning operations by comparing the network provisioning data against expected provisioning patterns, and
• wherein the anomaly detection model detects from the plurality of service providers one or more issues for the wireless telecommunications network;
• in response to detecting the one or more anomalies, generate an alert,
• wherein the alert indicates a type and severity of the one or more anomalies,
• wherein the alert identifies one or more affected service providers from the plurality of service providers and network elements responsible for the one or more anomalies;
• trigger a corrective action based on the one or more anomalies detected,
• wherein the corrective action comprises at least one of the following:
• adjusting network provisioning parameters, disabling a failing provisioning cluster to prevent further service degradation, redirecting provisioning requests to an alternative functioning cluster, or deploying a software patch via a continuous integration/continuous deployment (CI/CD) pipeline to resolve a detected issue;
• update the anomaly detection model continuously by integrating real-time network provisioning data, recorded anomalies, and corrective actions into a feedback loop,
• wherein the updated anomaly detection model improves in accuracy by adapting to newly detected anomalies and refining future anomaly detection patterns.

A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:

• receive network provisioning data from a plurality of service providers each supported by a wireless telecommunications network,
• wherein the network provisioning data includes service requests and/or provisioning status;
• segment the network provisioning data per service provider and based on at least one of the service requests and/or provisioning status;
• process by the network provisioning system, the network provisioning data using an anomaly detection model,
• wherein the anomaly detection model identifies one or more anomalies in provisioning operations by comparing the network provisioning data against expected provisioning patterns, and
• wherein the anomaly detection model detects from the plurality of service providers one or more issues for the wireless telecommunications network;
• in response to detecting the one or more anomalies, generate an alert,
• wherein the alert indicates a type and severity of the one or more anomalies,
• wherein the alert identifies one or more affected service providers from the plurality of service providers and network elements responsible for the one or more anomalies;
• trigger a corrective action based on the one or more anomalies detected,
• wherein the corrective action comprises at least one of the following:
• adjusting network provisioning parameters, disabling a failing provisioning cluster to prevent further service degradation, redirecting provisioning requests to an alternative functioning cluster, or deploying a software patch via a continuous integration/continuous deployment (CI/CD) pipeline to resolve a detected issue;
• update the anomaly detection model continuously by integrating real-time network provisioning data, recorded anomalies, and corrective actions into a feedback loop,
• wherein the updated anomaly detection model improves in accuracy by adapting to newly detected anomalies and refining future anomaly detection patterns.