Matcher

Reconciling events at different points in the system architecture is a key control for Revenue Assurance. This can be an onerous activity to undertake, especially where certain differences are perfectly legitimate:

• fields are reformatted, rounded, truncated and repositioned
• records can be filtered, dropped, merged or suspended
• system clocks used to generate date-stamps might be out of synchronisation

In addition there is the extremely large volume of data to manage.

Matcher is Ascertain’s event reconciliation solution.  It ensures completeness of processing and the proper application of business rules.  A key component of Matcher is the ability to match one format of EDR with a number of other differently formatted downstream records. Thus it can provide an operator with the ability to compare previously irreconcilable source of data.  At the heart of Ascertain Matcher lies a configurable, flexible correlation engine that can locate and match the same event in different files or database tables – even if recorded in an entirely different way.

Processing millions or billions of raw event records efficiently calls for specialist integration tools such as the Ascertain Event Processing Subsystem (EPS).  This module is used to handle the complexity of event record formats in an optimal way, passing the results through to Matcher for reconciliation.

Features and Benefits

Matcher is simple in concept but highly complex underneath.  It is used to correlate records from different systems such as a mediation device and a billing system, or a pre-pay platform and a switch.  In theory, it should be easy to correlate all such events and report those missing and mismatching.  Practical problems complicate this process:

• more records are switched than are billed – for example, transit records are typically filtered out of the retail stream.  Matcher has filtering functions which mimic the relevant business rules found in mediation and rating
• latency – events take time to pass from network to billing, so unmatched records do not necessarily signify a problem immediately.  The matching function is adapted accordingly, accepting records in any order and over a period of time.  Matching results are updated continuously
• data volume – quantities to be reconciled are often very large, therefore the correlation algorithm has to be highly efficient
• imprecision – some attributes may be required to match exactly, for example, the calling number or event type.  For others we can specify fuzzy matching to allow for clock drift, rounding and so on

Matcher successfully addresses these practical issues, allowing very high match rates to be achieved and rapid identification of any event leakage.

Screenshots

Matcher’s reconciliation and reporting capabilities allow rapid and detailed analysis of event data.  Exceptions can be clearly identified.