5 Key Use Cases for Anomaly Detection in Telecom Revenue Assurance
With 5G adoption and technology enhancement, the telecom operators are expecting an exponential increase in the data volume, the hardware footprint, and the OpEx required to perform daily revenue assurance activities. With the observed trend of falling ARPUs year on year, it has become evident for the telecom operators to look for a newer approach in Revenue Assurance practices.
Anomaly detection has the ability to monitor and detect anomalies on revenue metrics. This helps to provide deeper insight into revenue trends and business performance beyond what traditional BI tools deliver. To discover and understand how to prevent revenue leaks, revenue assurance teams require granular data, which requires weeks of work to combine, analyze, and correlate. While systems can save data for up to 60 days, figuring out what’s going on requires weeks of collaboration between engineering, big data, billing, and customer teams. Anomaly detection allows the processing of aggregate data based on specific dimensions to monitor millions of KPIs, identify discrepancies quicker and correlate with other associated datasets. The approach uses lesser hardware and provides near real-time insights to only KPIs, which ultimately helps to save on Total cost. Following are a few areas where Anomaly detection is proving to highly effective for Telecom operators:
1. Preventing Price Errors
In the telecoms industry, price errors are one of the most common sources of revenue leakage. Despite the fact that shops have implemented different checks and balances, pricing errors are widespread. The main contributions are data entry errors, missed decimal points, digit reversal, and other clerical errors done in a hurry. It can also happen due to misfeeding promotional offer dates, such that promotions may start or stop earlier or later than planned. Such revenue leakages go unreported at first but are discovered after they become a big problem. Automated anomaly detection can help telecommunications monitor sales price, volume, number of transactions, visitors, and other components in real-time and correlate them based on region, demographics, and behavior.
2. Monitor CDRs
Every time a subscriber uses a service via their phone, a call detail record (CDR) is created. These CDR files are subsequently transferred from the network to the mediation system, which then sends them to the billing system, which is in charge of processing CDRs and billing customers for their usage according to their agreed-upon plan. Telecoms may observe CDR leakage in revenue assurance solutions between multi-vendor mediation and billing systems. Although each correctly processed CDR generates income for the service provider, the billing system can drop or suspend some CDRs, resulting in direct revenue leakage. The machine learning model detected an anomaly whenever the response time of any billing API was beyond the normal range. Anomalous behavior was also detected in the infrastructure layer, such as irregular CPU use on a Unix virtual machine. The operations team responded by changing the billing API and repairing the Unix virtual machine in response to these abnormalities to prevent telecom revenue assurance.
3. Contract Analysis in Revenue Assurance Solutions
Aside from the contract terms, which must be fulfilled in the letter to avoid income leakages, the additional provisions inside the contracts lead to the organization creating value or leaking revenue. When a business creates and amends hundreds of thousands of contracts, it becomes practically hard to dive into the specifics and manage the contracts flawlessly, resulting in value realization and the avoidance of losses. While there are a variety of reasons for revenue leakage, the most common ones are data entry errors, unpaid accounts, client management issues, incorrect reporting, and discounting. All these are rooted in a lack of visibility, transparency, automation, and accountability, which have a significant financial impact on the company’s telecom revenue assurance. Anomaly detection identifies the anomalies in the form of errors and accuracies and helps in saving the total cost for the company.
4. Contract Monitoring
Preventing revenue leakage necessitates constant monitoring of contracts by revenue assurance solutions to create, amend, and implement them. Income leaks can be identified, recouped, and prevented by evaluating contracts and rigorously analyzing processes. Artificial intelligence (AI) is gaining traction across business operations and processes, including contract analysis and management, thanks to technological advances such as machine learning, natural language processing (NLP), and text analytics.
Due to network data’s complexity, dynamic nature, and newer 5G services, AI/ML-based autonomous solutions are essential for attaining business goals and avoiding blind spots. Dashboards and manual criteria aren’t responsive, robust, or agile enough to handle this challenge. Anomaly detection solutions can analyze several dimensions of data sources, looking at the cell, subscriber, and device-level KPIs. It can also effectively monitor network equipment defects, correlate alarms for noise reduction, and prevent revenue loss with root cause investigation. With falling ARPUs, the discrepancies quantified are not justifying the investment made towards onboarding 5G services into the traditional revenue Assurance practices; hence it makes Anomaly detection critical for the telecoms to meet the complexity of reaching the end goal in the Digital Era.
Read the Whitepaper on Deconstructing Telecom Revenues and Cost for Improved Profitability.
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