Redefining Rating Assurance with Analytics

Significance of Accuracy in Rating Assurance 

Revenue Assurance has been accepted by the telco world as a critical solution to improve data quality and processes. The solution can help CSPs increase profits, revenues, and cash flows and gain a strong Revenue market share without influencing demand. Globally today, most of the Revenue Assurance controls managed by the RA teams focus on the completeness of data used by business processes focussed towards revenue generation, thereby confirming the undisputable value the function brings to the business.

However, let’s check some other dimensions relevant to RA. TMForum RA guides show the completeness, validity, accuracy, and timeliness of data quality. To delve deeper into accuracy, a very critical aspect for any RA function; most of the time, accuracy is associated with quantitative measures attached to the charging and rating processes such as: duration accuracy, data volume accuracy, or rating accuracy.

In this article, we aim to look closer into rating accuracy specifically. According to RAG (Risk Assurance Group), “Data records accurately describe the event or object in the real world that they correspond to.” As a result, the rating accuracy of a tariff applied for a usage event or to a recurring or one-time fee is consistent with the Marketing teams’ intent.

Limitations of the current approach in Rating Assurance 

To date, rating accuracy is achieved by using a complex approach that consists of re-performance of rating processes and then comparing the output of the parallel rating with the actual rate. This approach comes with a set of limitations:

Sample-based method: There is a sample of usage events utilized for a selection of price plans. Though the tried and tested 80/20 rule is a powerful way of cutting through the clutter around a complex decision the outcome remains only as a sub-set of the rating universe tested.

High efforts required: Configuration of a rate plan, an add-on to a rate plan, maintaining it, etc., are activities which require massive efforts.

Sampling criteria: It is difficult to identify and arrive at valid sampling criteria, i.e., widely agreed and approved by the company. At the same time, these criteria should be regularly updated to avoid sample bias (providing assurance over the subscribers in the sample).

Shift from traditional to analytics-based accuracy for rating assurance 

Due to the above-mentioned limitations, it becomes necessary to identify and execute alternative ways to deal with uncertainties coming from rating accuracy. However, the alternative way has to satisfy several objectives, to be considered effective in validating rating process accuracy. A few of the goals are listed as follows:

• Ability to reuse existing data:Rated events are already processed, but the usage remains very limited where the central part is only utilizing completeness controls of a RA solution.
• Ensure complete data usage:The validation is required to be performed for all rated events and for all rate plans. In statistics, this would mean analysing all the population value of measurement, rather than its sample measure only.
• Perform Statistical measures:Revenue assurance stayed away from statistics for a long while. While sampling is not easy, it shouldn’t be understood that statistics address only decisions under limited data. Measures like mean, median, mode, standard deviation belong to statistics 101 and can be efficiently used to enrich RA’s data.

Can this analytics approach be accepted as a new Future approach? 

Definitely, the statistical approach is not new, as it has been used for ages now, to develop quality controls across industries, advance science, and advising on decisions. But fundamentally, the use of statistical controls for validating rating accuracy is proving to be “new.” It may not be a one-size-fits-all approach, and it may not give the same sense of control as that of a re-rating approach however, it shows promising results, particularly for RA teams, which have limited resources to maintain a parallel rating configuration, as required by the standard approach. A few examples of rating accuracy issues can be identified in this way:

• Different complex implementation ways of tariff and rates from the usual defined
• Inaccurate international and roaming charges for voice and SMS
• Mobile data roaming charges applied incorrectly.
• Unbilled add-ons
• Free of charge out-of-bundle usage

Analytics can help solve several sets of the above limitations. Subject matter experts in the area of rating assurance have found that an analytical approach can be very efficient while comparing the limitation of traditional methods.

The way forward

In a nutshell, A Revenue Assurance Solution combined with Advanced Analytics, which powers the solution ability and agility to manage and generate insights using large volumes of data, can be a winning approach. This doesn’t mean sunsetting the advanced rating engines from the RA solutions but letting analytics do what they do best: add value from the wealth of data the telco operators are sitting on.

Download our Point of View to learn more on How AI Can Complement Business Assurance for 5G

Download Here

Stefan Buliga

Stefan has more than 17 years’ experience in telecoms risk management, helping telecoms organizations navigate risk and technology. He has worked on various roles related to Revenue Assurance, Fraud Management, Credit Risk Analytics solutions as a consultant, trainer, subject matter expert, and project manager for various operators worldwide. Currently he’s a principal consultant in Subex’s Business & Solutions Consulting European team.