01Challenge
Identifying Unusual Usage and Billing Activity Within Large Volumes of Data
Telecommunications services generate various types of data, including usage times, data volumes, connection locations, service plans, and billing amounts.
Because normal usage patterns differ depending on the customer and contract, simple rules may not accurately identify all activity that requires further review.
Reviewing every usage record and billing detail manually would also require significant time and personnel.
02Solution
AI Detects Patterns That Differ from Normal Activity
AI can analyze historical usage patterns and billing data to identify activity and charges that differ significantly from normal patterns.
For example, providers can streamline processes such as:
- 1Usage and Billing Data Collection
- 2AI-Powered Pattern Analysis
- 3Potential Anomaly Detection
- 4Risk-Based Prioritization
- 5Staff Notification
- 6Investigation and Response Recording
AI identifies data that requires further review. Staff then examine the relevant information and determine whether fraudulent activity has occurred and how to respond to the customer.
03Use Cases
Potential Applications
- Detection of sudden changes in data volume or usage time
- Detection of usage from unusual locations or devices
- Identification of usage patterns that do not match contract details
- Detection of unusual increases or decreases in billing amounts
- Support for identifying duplicate charges and calculation differences
- Prioritization of cases requiring investigation
- Recording of detection, investigation, and response histories
- Reporting on anomaly trends
04Expected Benefits
Operations That Respond Quickly to Information Requiring Review
By using AI to identify potential anomalies within large volumes of data, staff can focus on investigating high-priority cases instead of reviewing every record.
Continuously collecting investigation results can also help improve detection rules and analytical models, increasing the accuracy of review operations.
Robo Lab uses AI, automation, and data analytics technologies to improve fraud prevention, billing reviews, and risk management operations for telecommunications providers.