Dave Stuart

David is the Assistant Director of Revenue Assurance and Fraud Management at Qtel International. A Revenue Assurance expert with over 10 years practical experience within Telecoms RA. David graduated from Brunel University with a Mathematics and Statistics degree, after which he moved into the financial services sector.

David started his RA career at MCI WorldCom, where he designed and project managed the development of a traffic assurance tool covering a dozen business units fixed line traffic.

From here David moved to One2One/T-Mobile where he provided architectural assurance over all of the company’s developments. David’s expertise in the company’s architecture led him to be made Test Manager over the UK’s first 2.5G launch.

From T-Mobile David moved to specialist RA consultancy 2Helix, where he held the position of Senior RA consultant, he worked on numerous projects all delivering RA software, often bespokely designed for the clients needs.

After 2Helix David moved to Cable and Wireless International, where he was again responsible for delivering RA tools, this time to CWI’s 33 business units.

For this weeks question, I want to discuss one of the most annoying parts of working in RA – When data doesn’t link. A typical example is a network to billing reconciliation of the ADSL product. Typicallly a DSLAM will only contain information like dslam id, port, slot and maybe an IP address whereas the billing system will generally have some sort of service identifier along with associated billing information. The data as such is un-linkable and therefore would appear non-reconcilable – what would you do in this scenario?

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There are a number of ways you can find quick RA wins within an operator, the following are just a few tips for routing out the low hanging fruit:

1) Existing Reports - Most departments produce there own reports detailing customer numbers, system throughputs, revenues etc.  Take these reports and compare the numbers ie NOC customer numbers vs Finance customer numbers – quite often you’ll find large differences that indicate that some sort of problem exists

2) Interviews – Try and identify the key personnel who have manual processes that are Revenue critical – ie someone who manually inputs service orders. Interview the SME and you may find serious control weaknesses that indicate a potential revenue loss

3) The Internet – This is my favourite and the easiest way to find revenue loss(fraud). Go online and search the internet for free calls with your operator. You’ll find a number of chat sites where people swap tricks for getting free calls – test out what they say – you may get lucky.

4) Customer Services – When customers get over-charged they’ll complain, these complaints will be logged in a CRM system and hopefully categorised.  Try and identify the most common complaint – obviously what you’ll have is a Revenue Gain issue, but if the issue is the result of a system failure/problem you’ll often find that the inverse issue exists whereby customers are being under-charged.

The above are just a few tricks that can be used, but the basic theme is simple – when faced with short timescales, don’t try and source your own data just use what’s already available.

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This weeks question is more of a general discussion topic, which I would be very interested in hearing peoples opinions.

As a consultant I am often asked to find a quick win, either to demonstrate a teams capability or to get executive buy in. To do this I have a number of tricks/tactics that can often flush out a small amount of revenue loss.

I’d like to know, how other people deal with this type of request

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For this weeks question, the route cause was one of many possibilities that were investigated. The actual issue was identified after validating data used for performing a prepaid balance audit.

A prepaid balance audit is a simple control whereby the following steps are conducted:

Step 1: At a fixed point in time record the total amount of credit held on your prepaid billing system

Step 2: Record the total number of credits(top ups) to the prepay billing system for a given period(say 24hrs) – use an external source like the prepaid card database along with any other crediting mechanisms

Step 3: Record the total amount of debits from prepaid accounts for the same period(as a negative number)

Step 4: at the end of the period record the total credit held on the prepaid billing system

Step 5: Perform the following reconciliation:  1 + 2 + 3 = 4

 

Now the above didn’t identify the issue, however by validating Step 2 against cash received for sales of top up cards we could see that there was a general issue, where more credits were being applied to the prepaid billing system than those being sold.

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For this week’s question I’m going to return to the subject of fraud and it’s cross-over into Revenue Assurance analysis.

Whilst working for a mobile phone operator, it was identified that someone in the prepaid top up card printing factory had been copying down the card’s unique codes and topping up their own account.

How would you identify this issue?

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To resolve this week’s issue, we went for the most obvious resolution path and performed a CDR to SS7 reconciliation against a days worth of traffic. After performing the reconciliation, we were still left with a large amount of SS7 records with billable duration that didn’t match to CDR’s.  So we then started to analyse these SS7 records and the underlying customers ie

Had the customers produced any CDR’s – Yes,

Were calls to the specific destinations producing CDR’s – Yes,

Was it a specific duration issue – No,

Was there a unique flag on the SS7 record – No,

Did the SS7 records just originate from one switch – No – but not the entire network.

It was this last point that started to hone us in on the actual issue, all of the additional SS7 records came from a specific region of the country, which was served by 8 switches.  According to the company’s engineers, there was nothing unique with this part of the network, so it was back to the analysis.  At this point we started to look at individual customers calling profiles and it was when we did this that we found the problem. We effectively had duplicate SS7 records – although they weren’t true duplicates in that the call times and durations were ever so slightly different. We had duplicate checked the data right at the beginning of the analysis (as any good analyst will do), however because the records were different – they weren’t flagged up to us. 

Each pair of SS7 records were being recorded by different probes, the overall SS7 solution worked in such a way that only the originating probe generated the dummy SS7 CDR, so the fact there were two records, both stating they were the originating probe, suggested something was wrong with the network ie double call routing. At this point the issue was handed back to the engineers to work out what the cause was.

The network had originally operated an “A-Link” signalling network (signalling that goes down it’s own pipe via an SCP), however the operator had moved to an “F-Link signalling network (signalling is carried by the same pipe as the call) a few years prior to the investigation described above.  The old A-Link signalling was supposed to have been fully decommissioned once the new F-Link was put in place, however it transpired that in this region of the country, the decommissioning had somehow been missed. So what was happening, was that for each call being made, the signalling was being sent across two pipes and hence two originating SS7 probe CDR’s were being made -as there was a probe on every part of the signalling network(I know – you would have thought they would have spotted the issue when installing the probes!!)

An SS7 to CDR reconciliation is one of the best pieces of analysis an RA department can perform – it allows us to verify the one piece of information we rely on for the majority of our other reconciliations (the switch CDR). Ultimately though, I hope the above issue has demonstrated that an SS7 record cannot be treated as definitive and does require the same levels of authentication as any other record produced by a network.

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