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Splunk Essentials for Financial Services Industry v1.1 Release

Last year, we released Splunk Essentials for the Financial Services Industry, a learning app that contained 15 use cases and 94 examples for using Splunk in FSI. All the user had to do was install the app on their search head, stand-alone Splunk Instance or laptop, and start learning. In that same spirit, version 1.1 is released and on Splunkbase ready to download for free.

The new release has two new use cases covering 50 new examples. The first use case is called Banking Fraud, which deals with detecting possible fraud situations for consumer and retail banks by correlating events with thresholds that are known to trigger alerts. The input data set for most of this can come from proprietary application logs or in some cases, web logs.

Banking Fraud (19 new examples) — here are some highlights:

  • Paper hanging
  • Duplicate check detection
  • Suspicious simultaneous withdrawals
  • Large withdrawal after a profile edit (Thanks, Jim Apger, for the suggestion)
     

The second use case involves Mobile Payments, which covers statistics and analytics for users using mobile apps for making payments. The mobile payments may also use partners of the bank to underwrite the payment. Statistics covering those partners (for which we use fictional names in the examples) are presented as well. Some of the examples utilize elementary machine learning techniques to find outliers in response times, without using pre-built models. This is because Splunk is rich with a set of commands that can be used for this purpose. The input data set for most of this can come from custom mobile application logs, proprietary APIs, or Splunk Mint in some cases.

Mobile Payments (31 new examples) — here are some highlights:

  • Average response times by vendor partner, mobile OS, or location
  • Payment amounts split by vendor partner in the 95 percentiles (Thanks to Tom Lagatta for the suggestion)
  • Outliers in response times
  • Originating locations for mobile payment shown in map
  • Last action performed before a crash
     

If you found last year’s release of this app helpful, we hope this year’s new examples will be equally useful. Happy Splunking!

Nimish Doshi
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Nimish Doshi

Nimish Doshi is a Principal Systems Engineer with Splunk and has been active on Splunkbase content creation and writing Splunk blogs entries for a number of years.

 

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