Holiday dinners have a familiar cast of characters that most can relate to—from the grandparent heralding the good old days to the little cousin who somehow can work a smartphone better than many of the adults in the family.
Performing real-time outlier detection to identify fraudulent credit card purchases using DBSCAN and Deephaven
Credit card fraud causes billions of dollars in damages each year. The most infamous cases have affected tens to hundreds of millions of consumers in single attacks through the unlawful exposure of personally identifiable information (PII) related to credit cards. Isolated cases are also common, and can be caused by a variety of methods including skimming, social engineering, and application fraud.
In order to protect their customers, credit card companies rely on fraud detection and prevention software to analyze credit card purchases. These programs look for unusual or unexpected patterns to classify them as possibly fraudulent. In this blog, we propose our own real-time credit card fraud detection solution using Python with Deephaven.
The code in this blog uses SciKit-Learn, which does not come with Deephaven's base images. To run this code, ensure you have the module installed. Here's how you can Install Python packages and Use Python packages in queries. We also have a guide for How to use SciKit-Learn in Deephaven.
And how to deal with them...