Did you realize that in banking, a mind-blowing 56 billion dollars was the total fraud loss for the year 2020? Of course, 2020 had far fewer transactions than in previous years making the dollar amount even more daunting. This GitHub repository will provide a simple example of alerts for streaming fraud and anti-money laundering in a streaming banking use case.
This example showcases SAS Event Stream Processing (ESP) and focuses on the following topics:
- Pattern matching
- Temporal sliding windows
- Data aggregation
Since this example doesn’t use advanced analytics, it is perfect for those of us who are not analysts. It does simple data comparison and analysis using rules, predominantly with temporal sliding windows. It produces alerts and notifications using only pattern matching, temporal sliding windows, and aggregation.
The GitHub repository provides you with everything you need to run the example yourself. There are step by step instructions, prerequisites, and other information included. Additionally, the logic behind the scenes is explained in detail.
Please click this GitHub link and start learning!
Whether you're already using SAS Event Stream Processing or thinking about it, this is where you can connect with your peers, ask questions and find resources.
Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.
Find more tutorials on the SAS Users YouTube channel.
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