Real time fraud detection is one of the use cases, where multiple components of the Big Data eco system come into play in a significant way, Hadoop batch processing for building the predictive model and Storm for predicting fraud from real time transaction stream using the predictive model. Additionally, Redis is used as the glue between the different sub systems.
In this post I will go through the end to end solution for real time fraud detection, using credit card transactions as an example, although the same solution can be used for any kind of sequence based outlier detection. I will be building a Markov chain model using the Hadoop based implementation in my open source project avenir. The prediction algorithm implementation
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