Category: Business Assurance

In the highly competitive and rapidly evolving landscape of the telecom industry, telecom operators are expected to expand significantly to bring in optimised profits. In this context, revenue assurance has greatly evolved in its role as a business controller to a business enabler.

Revenue reporting is a critical area of CSP business. Here, operators face severe challenges when it comes to computing revenue across earned and unearned revenue, compliance, recording of financial data. The operators also find it hard to identify leakages within order to cash processes, voucher generation (electronic and physical), starter kits and handset bundles, pre-provisioning processes (shipment of stock and invoicing of channels), etc., leading to miscalculation, siloed process, inaccuracies in customer collection data or overstatement/understatement of revenue.

Accounting validations to identify revenue leakage

The existing sales process across all channels for prepaid and post-paid services comprise crucial configurations and validations. When clearly documented, these validations provide important insights into potential areas of revenue leakages within an operator’s accounting activities. The ‘need to follow’ configurations and validations for prepaid and post services is described below:

 Prepaid validations

•  End-to-end process validation: Involves computation and validation of the complete process from demand forecasting to voucher generation, voucher transfer, starter kits to warehouse etc. Every single discrepancy observed in this process gets highlighted with the result of the end-to-end support performed in the validation.
• Stock management review: It helps understand and measure the entire stock management process at the warehouse for recharges as well as starter kits including voucher or roaming recharge packs. If gaps are identified, a mitigation plan can be recommended.
• Distribution process review: This involves a fulfilment process review for all recharges and starter kit sales. It is majorly recommended to identify issues around distribution
• Invoicing process validation: This validates the invoicing process for sale of recharges and starter kits. It can highlight issues around invoice quantity, denominations, tax, etc.
• Account receivables review: This helps understand the account receivables for all vendors and compare this against the bank guarantees to derive the net receivables. Through this, operators can identify those vendors with net receivables that increase bad debt
• Independent method analysis: This involves independent calculations of liability based on billing methods and compares these with financials to identify discrepancies. It helps operators analyse the product constructs and their impact on deferment of revenue
• Report and transaction analysis: This includes an independent review of revenue by analysing IT reports, usage transactions, subscriptions, other adjustments, expired vouchers/balances, and deferment logic for monthly rentals. It gives operators a holistic overview of revenue flow and liability.

Postpaid validations

• Configuration validation: This validates the configuration of charge code to GL code between Billing system and ERP for accurate integration of automation revenue flow
• Billable versus billed validation: This facilitates reconciliation to ensure that all subscribers have participated in the invoicing process.
• Invoice to ERP: Validation of invoice amount billed in the system against the account receivables created in ERP system
• Unbilled revenue: This validation compares usage and other components against reported numbers. It considers analysis of all the components to bring the result in a form for a detailed comparison.
• Financial reporting: This provides an analysis of postpaid revenue streams across all billing cycles. It helps to understand and highlight the concerns if they occur.

Current revenue accounting process have in-built mechanisms to identify discrepancies that may lead to revenue leakages. These mechanisms or validations are based on operator group guidelines, review of amounts credited to subscribers, and revenue realized through usage recorded in charging systems. Careful comparisons between different parameters can alert revenue assurance teams about potential leakages, thereby minimizing variances in the earned and unearned revenue computations across prepaid and billed and unbilled revenue computation for postpaid.

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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.

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Digital disruption, technology innovation, and the hyper-evolving telecommunication market influence a CSP’s ability to maintain long-term profitability.

Margin assurance is a field that draws on many streams to balance costs and spends. It includes handling a complex mix of service offerings, defining a holistic view for cost allocation, enabling real-time visibility, margin automation, etc.

What makes margin assurance a key ask for CSPs today? Here are five reasons:

1. Unidentified contributory costs in a multi-partner ecosystem 

With the rise in debt collection, depreciation, and other activity costs, it is becoming increasingly difficult to optimize gross regulated margins. The proportion of different direct and indirect expenses increases as the number of partner systems increase. Improper cost allocation models, in addition to the bundling of network and business costs, increase the complexity of costing.

2. Deficient view of capacity, utilization, and profitability across network elements 

While operators dedicate significant spend on planning and capacity optimization, they also need to keep an eye on profitability. Profits, too, must grow along the network value chain. This is done by analyzing multi-channel provisioning behaviour to determine the best way to improve margins and optimize utilization.

3. Absence of granular level visibility into costs and revenue parameters 

Technology advances are encouraging product innovation for next-gen communication needs. CSPs must identify different product costs and map them to revenue to fully understand the total cost for each product, customer, and business segment. With limited insights into the cost and revenue segments, CSPs struggle with cost allocation parameters. Telecom employees need the right skillsets in cost accounting and telecom knowledge to map the cost of individual products or bundled segments of voice and data traffic. Discrepancies in profit increase with the inability to determine the accurate price of a particular product or technology.

4. Lack of real-time insights into new-age product profitability, pricing, and performance 

With data on the rise, CSPs must bridge the gap in data reconciliation across various sources such as networks, partners, and products. Dearth of insights into the real-time performance of products can negatively impact the organization’s profit and loss. Strong data management increases efficiency in identifying and tracking the profitability of services, devices, and other entities, cementing the need for margin assurance.

5. Bundled Products & technologies results in complex cost allocation models 

Operators are trying to merge/bundle multiple services and technologies, resulting in the creation of complex cost allocation models. This leads further product complexity and revenue differentiation due to the bundling of multiple products and technologies. It is important to give a strong consideration to the product line and build cost allocation models in line with industry-leading practices support to arrive at near real cost allocations for profitability computation.

Margin assurance enlists the metrics to handle the bottom-line complex mix of service offerings defining the holistic view for cost allocation, real-time assurance, margin automation, etc. This helps to achieve the benefits in terms of ‘profit computation,’ ‘flexible cost allocation,’ and ‘effortless cost modelling,’ which further lead the operator to widen its scope of work with a massive increase in the number of cost line items

Margin Assurance To Ensure Profitability For New-age Telcos

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Revenue assurance (RA) is a common business activity for telecommunication service providers. It utilizes data quality and process improvement methods to increase profits, revenues and cash flows without influencing demand.

In 2008, when I began my career journey in the telecom industry, I observed that telecom service providers mostly focused on the aspect of revenue enhancement with the aim of gaining strong revenue market share (RMS) and customer market share (CMS). This was done to augment their coverage in almost all the circles across India. ‘Circles’ refer to service areas, largely divided according to states/population index by the Telecom Regulatory Authority India (TRAI). The point to note is that there was not much of a focus on cost reduction as an objective.

At this time, most organizations did not possess any specific tool for revenue assurance either. Even if they did, it was rarely geared to the enterprise needs, leading to sub-optimal performance. For some, revenue assurance fell under the sole purview of finance departments.

Without a focused RA tool, risk assurance teams (or people like me in my earlier days) used to sit for half a day to manually execute revenue assurance activities in MS Excel or Access!

In some cases, performing control activities, reconciliations and analysis of exception needed a few more hours, meaning the day was almost over by the time the reports were prepared and the final consolidated dashboard was circulated to other teams like network, marketing, service provisioning, etc., for taking the necessary corrective actions. One dismaying effect on such delays was that case closure would get pushed to another day. A study by TM Forum underscores this inefficiency: it showed that 1% of the gross revenues are lost due to open cases pushed to the next day. Significantly, this excludes losses due to fraud.

Thus, lengthy turnaround time for case management impacts the entire organization.

Compared to the relatively nascent field of revenue assurance among small and large telcos in India, the area of fraud management was well understood. This here was a mature field that was seeing significant growth with many telcos outsourcing fraud management tasks to external vendors like Subex.

Fast forward to more than a decade later, CSPs are now always in a hurry to release modern features and keep pace with what’s new in the market. But while they do this, they must also ensure profitability. Having worked with some of the leading telecom operators across India, I believe the industry needs revenue assurance systems or tools that save manual effort by performing automatic reconciliations across a gamut of technologies, services and products. Armed with these tools, revenue assurance experts can focus solely on analytics and close cases with timely corrective actions.

AI/ML-based Revenue Assurance solution helps telecom operators get revenue assurance automated and data driven capabilities at low latency. Apart from automated reconciliations, the solution also delivers output across all revenue streams such as prepaid, postpaid, interconnect, data, VAS, roaming, etc., giving risk assurance managers a holistic and accurate picture of revenues.

Most important capabilities of an AI/ML-based Revenue Assurance solution include:

• Data gathering from all sources
• Reconciliation/auditing
• KPI dashboard
• Leakage analysis
• Operational workflow management
• Problem correction
• Business reporting

This solution generates tremendous value for customers by bridging the gaps and addressing the challenges of traditional revenue assurance processes.

Automated reconciliation truly gives telcos a sharper edge for cost reduction as well as revenue enhancement.

GCC operator leverages Subex ROC Revenue Assurance to improve margins and profitability

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Now that I have your attention, I’ll move to the actual topic. Ever since I’ve started heading up the Revenue Assurance product line at Subex, one question has inevitably been asked by almost everyone I meet – What would the RA product look like <Insert preferred number> years from now?

Don’t get me wrong – this is exactly the kind of conversation that I find stimulating. I find that letting your imagination run wild actually helps you find the perfect, ideal solution. Now from that point on, it’s a matter of identifying the key roadblocks and challenges and trying to overcome each one – or finding an insurmountable one, which means we go back to redefining the end-state.

In my thought journeys so far, I have three candidates so far who I believe are the greatest things since sliced bread (if sliced bread was RA). Each of the following options are based on three distinct challenges which I would love to overcome, namely

• control/influence over the whole BSS/OSS roadmap,
• a genius level understanding of cyber-ecosystems and of course,
• my personal favorite, the complete trust of all the subscribers of my customer.

Do feel free to post in the comments which one is your personal favorite, or better yet, your own thought journey. Now onto the great reveal! My top three candidates (in a utopian environment for RA professionals) for the future of RA systems are:

• Self-Correcting Telecom Stack – Now to achieve this, I only need to be bigger than Huawei. In a nutshell, this idea involves OSS/BSS systems which talk to each other on an on-going basis. So if a customer of the network is getting frequent dropped calls, they automatically get upgraded to a priority slice with the CRM sending across a “Sorry, my bad! No more dropped calls for you today!”. Another fun situation – if I see my service is deteriorated from a 100 Mbps to a 25 Mbps for some customer, then it automatically sends a message to the billing platform to work at a lowered rate until normal service resumes (and of course the message to the customer, ”Sorry John, my network planning team was incompetent in terms of guesstimating the actual peak capacity. We’ve reduced your billing rate and given you 20GB top-up for each day you see this”). I’m talking about network switches and charging platforms and partner settlement systems actually talking to each other in terms of assuring integrity and correctness. This approach would truly bring back the “customer” to the term “customer-centric”!
• Bot-net based auto-correction – Lets assume for the moment tat being bigger than Huawei takes a little more effort than keeping your new year resolutions. In that scenario what can we do? Me, I like to take a leaf out of the IT operations world – we use intelligent agents on all network elements. Let call them Bots, because it does a little more than monitoring key KPIs. The idea is similar to the previous one, except instead of each element having a built-in communication heartbeat protocol, we have a bot-net deployed across your entire operations. Now what happens in afore-said bot-net? Well, every single update to key configuration or profile parameters is communicated in byte sized messages through the bot-net to all relevant up/down-stream elements. For example, in case of a dynamic tariff plan change, the bot at the provisioning element communicates with the bot residing on the charging element as well as the bot on the network side. The charging system bot verifies that for that particular subscriber, the rate plan has been changed and the network side bot then confirms that the QoS allocation for that subscriber has in fact been upgraded. We can also have PCEF/PCRF bots to verify aspects about Fair Usage policy, max up/download limits etc. What a wonderful world we’d live in where the only possibility of any mismatch happens when the bot-net is down!
• RA as experie nced – Okay, so maybe building an intelligent, communicative and corrective bot-net is not what I spend Sundays on. Is there no way forward for RA software – fret not, there is one more idea up our sleeve. What if, with the consent of all the subscribers in an operator environment, we send RA agents on the phones of our subscribers. The RA agent essentially monitors usage on the network, up/download volumes, services available, billing/charging profile hashmaps and essentially any network related faults. Imagine all these device-bots (or as I like to call it, the Dendrones) then communicate with the central server (or as I like to call it, the BRAIN). Discrepancies are identified on a per device/profile/event basis, and corrective actions are executed in event-time. This approach would give an outside in view to Opcos, something which may be sorely missing in most RA departments today. Most importantly, this would significantly obfuscate the complexities of the network and focus on the deliverable – Predictable, Consistent and Quality service to our subscribers. Tres MAGNIFIQUE!!!

Of course, these are the musings of someone who wants RA tooling to become simple enough to be comprehensive, and yet light enough to allow for dynamic expansion with minimal time to market for new controls. Do let me know in the comment section which option makes sense to you, or conversely, let speak about why something wouldn’t work. Either way this is a topic which I believe has crossed all our minds at some point or the other – collectively, we might find a new thought  journey which makes sense to all of us.

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The article was originally published at Commrisk