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Splunk Lantern

Payment SLA counts

You might want a count of how many payments did or didn't meet their processing SLA when doing the following:

Prerequisites 

In order to execute this procedure in your environment, the following data, services, or apps are required:

Example

A key KPI in the financial industry is how many transactions met SLAs and did not meet SLAs for duration/response time. Payments that take too long to process may be subject to fines and cause customer dissatisfaction.

To optimize the search shown below, you should specify a time range.  You may also need to adjust fields to match what is available in your data source. 

  1. Run the following search:
    |sourcetype=<payment processing data>
    |eval _time=strptime(_time, "%Y/%m/%d %H:%M:%S")
    |sort _time
    |stats count  first(_time) AS first last(_time) AS last BY sessionID
    |where count>1
    |eval duration=last-first
    |rangemap field=duration Met_SLA=1-1500 Near_SLA=1501-2200 Missed_SLA=2201-10000 default=Missed_SLA
    |chart count BY range

Search explanation

The table provides an explanation of what each part of this search achieves. You can adjust this query based on the specifics of your environment.

Splunk Search Explanation
|sourcetype=<payment processing data> Search only your payment processing data.
|eval _time=strptime(_time, "%Y/%m/%d %H:%M:%S") Parse the time stamp into a UNIX time value.
|sort _time Sort the results from oldest to newest.
|stats count first(_time) AS first last(_time) AS last BY sessionID Display the times of the payment request and response. Then group the payments by their unique identifier.
|where count>1 Filter results to those where the count is greater than 1.
|eval duration=last-first Create a duration field that is equal to the last minus first time.
|rangemap field=duration Met_SLA=1-1500 Near_SLA=1501-2200 Missed_SLA=2201-10000 default=Missed_SLA Set ranges for payment durations that missed SLA, met SLA, and nearly missed SLA.
|chart count BY range Display how many payments fall into each range.

Result

Visualizing the count of payments that did not meet your service level agreements due their long durations can help speed up investigations into why. Knowing how many payments that were processed missed SLAs, met SLAs, and nearly missed SLAs is a KPI to monitor for payments processing. Banks want to constantly know how many are missing SLAs.

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