Mediation

In revenue management, mediation is a process used to collect, validate, filter, collate, correlate, aggregate and convert data to create a data record that can be used for billing purposes. It is the first step in the billing process after collection of data records, and before Rating.

The data records produced by mediation were called Call Detail Records (CDRs), and contained all the necessary parameters to rate and bill a voice call such as call duration, distance, time of day and so on. Latterly, the record has become known as an xDR, IPDR, EDR and so on, to distinguish voice call records from records used to bill data services.

Historically, telecoms switches performed some of the mediation tasks, and mediation was often built into the billing solution. Mediation functions are not typically provided by a router, however. Thus with the move to data networks (packet switching), as well as the introduction of prepaid services and data services, mediation became far more complex. This resulted in a generation of specialist, standalone mediation platforms in the late 1990s and early 2000s. Over time most of these were acquired by billing vendors.

It should be noted, however, that data produced by mediation platforms is often used for non-billing purposes. For example, the mediation software might generate traffic volume statistics based on the number and origin of the records passing through it. Those statistics could then be used for capacity planning, as part of a network monitoring procedure, or for any other business intelligence applications. Other solutions such as Interconnect settlement, Business intelligence, Revenue assurance and Fraud management, Test Call Generation and so on, may use the data provided by a mediation platform.

Revenue assurance and Fraud management

Revenue assurance is the practice of identifying and remedying (sometimes preventing) financial underperformance in CSPs. Due to the complexity of revenue management, the complex nature of telecoms software infrastructure, combined with human error and suboptimal processes, CSPs 'lose' a percentage of revenue owed to them. Revenue assurance software, processes and practitioners seek to minimise these losses to the company and prevent them from occurring.

While revenue assurance processes and software seeks to minimise revenue lost due to incompetence or error, in contrast fraud management is the practice (aided by software solutions) of minimising financial losses due to deliberate or intentional theft (fraud).CSPs are subject to a number of types of fraud, and the types, scale and complexity of fraud continue to increase.

FRAUD
Fraud in telecommunications can be very complex and transversal to the operator structure. The authors propose a classification method used in the FMS case management that allows a better characterization of the fraud phenomenon and enables a detailed reporting. The approach used is based in the 3M’s classification:

Motive: the fundamental objective of the fraud.
1. Non-revenue fraud, making use of a service with intent to avoid the cost but without the intention of making money. It includes providing no-cost services to friends or private usage.
2. Revenue fraud, which intends financial benefits as in Call Selling or Premium Rate Service (PRS) fraud (described below).

Means: the nature or form of the fraud used to satisfy the motive. Some examples:
1. Call Selling: sale of high tariff calls – usually international – bellow their market value with the intent to evade the operator payment.
2. Premium Rate Services (PRS) Fraud: inflation of the revenue payable to a Service Provider by generating calls to a PRS line.
3. Surfing: use of other person’s service without consent which can be achieved, for example, through SIM card duplication (cloning), illegally obtaining calling card authorisation details or PBX hacking.

4. Ghosting: refers to obtaining free or cheap rate through technical means of deceiving the network. It can be performed, for example, by manipulating switch or database contents to ‘alter’ call records.
5. Sensitive information disclosure - involves obtaining valuable information (e.g. VIP client details or access codes) and selling it to external entities. This fraud is usually performed internally.
6. Content stealing: a more recent type of fraud, which deals with getting high value contents (videos, ring tones, games) for free, by exploiting the non real-time pre-paid billing pre-paid system (hot-billing) or by avoiding payment of the invoice (post-paid services).

Method: the generic fraud method.
1. Subscription: fraudulent subscription obtained with false credentials that allow debt accumulation by systematic payment avoidance.
2. Technical: more advanced fraud that is based in exploiting loopholes found in the operator network elements or platforms.
3. Internal: inside information systems abuse

4. Point of Sale: when the dealer manipulates sales figures to increase the compensations paid by the operator

A FMS should be able to collect data from multiple formats and sources, and through a process of data preparation and mediation, conveniently process and adapt it to the system internal data formats. Some of the relevant processes of this stage are data filtering, call assembly and call rating. With some FMS tools, it is possible to perform data enhancement through cross-relation of different data sources, which may boost performance in some more complex detection techniques.

Subsequently, detection processes are applied in order to generate alerts on situations that deserve closer investigation by fraud analysts. Some of the relevant techniques used in this stage are rule–based detection and profiling through Artificial Intelligence (AI) techniques like neural networks or decision trees.

Fraud analysts investigate alerts by accessing all relevant information (detailed client/account information, associated Call Detail Records, alert details, client alert history…) needed to conveniently assess the alert. Alert clarification may also benefit from graphic information describing client consumption profile.

Detected fraud cases are then forward to a case manager to initiate subjacent bureaucratic processes subsequent to fraud identification. All relevant information (e.g., CDR details, detailed client information, related alerts…) is attached to the case and the specific case fraud is classified (involved

services and fraud motives, means and methods) along with financial indicators quantifying performed fraud detection gains against fraud losses.

Finally, the system must provide friendly and complete reporting tools, thus allowing access to all relevant information to analyst, fraud process manager and system management information.

Fraud tackling efficiency may also benefit from seamlessly integrating and cross-referencing multiple data sources (client and billing information), which may enable focusing on most suspicious alerts.

Versatility and adaptability of fraudsters imply usage of different tools and technologies for each scenario. These tools must handle huge data volumes (e.g., billions of call records) and allow the integration of any new relevant technology. Additionally, the regular advent and new services and client growth implies easily scalable tools.

Billing, Charging & Settlement

Originally, telecoms billing was post-paid. In other words, a bill (originally this was generally a paper bill) was generated in arrears periodically stating what was owed to the CSP by the customer. The customer was then expected to settle the bill (payment). The bill would typically show a number of different charges, including "line rental" (a fixed charge to cover the cost of maintaining the line and the network) and "call charges" (a variable charge for calls made).

Telecoms bills were calculated according to a number of parameters. For example, the charge for a call would vary depending on factors such as the time of day, the distance and the duration of the call. Initially, consumer bills were typically summary documents, but later so-called "itemised billing" was introduced, which involves listing the charges for individual calls.

With the advent of mobile/wireless telecoms, a new type of telecoms service - prepaid - was introduced. This involved the customer paying a sum in advance, which was depreciated as telecoms services were consumed. The introduction of prepaid services necessitated a change in technology: the solution that supported "prepaid billing" needed to operate in realtime or near-realtime to ensure that customers could not use more services (and therefore incur more charges) than their balance permitted. Prepaid billing became known as "realtime charging" or "online charging" (to contrast with so-called "offline" billing systems that run in batch).

Realtime charging was typcially implemented using a completely separate infrastructure to the billing systems used to support postpaid or contractual customers.

Although post-paid and prepaid billing and charging were typically implemented separately, lately it has become necessary or desirable to offer more flexible and hybrid billing models. This has resulted in so-called convergent charging, which refers to solutions that can support different subscriber types, different networks and different billing models using a single convergent billing system. Increasingly, convergent charging suppliers emphasise the desirability of basing such as solution on a single data model.

Settlement is the term used to refer to the processes and systems that enable CSPs to pay, and receive payment from, partners and suppliers. This includes Interconnect settlement, as well as settlement with content providers, roaming partners and so on. Settlement solutions also often include billing capabilities, as well as Partner relationship management (PRM) functions, Business intelligence (BI) and so on.