Guest Post about Fraud Detection on AWS Blog

As this blog is growing more and more into a portfolio for my data science work I did not want to miss out on the opportunity to (shamelessly) promote my guest post on the AWS blog from November:

Advancement in Fraud Detection: ML in Online Survey Research

The fraud detection solution described in the blog post was part of a larger anti-fraud initiative at my previous company. Since the solution was highly successful in combating fraud and also made heavy use of AWS solutions (in particular Amazon SageMaker) our contacts at AWS asked us whether we would like to share our experience in a short post on their blog.

One of the main technical requirements that we needed to meet in this project was that the model endpoint had to accept a JSON payload as input. This payload had to be transformed into a numeric input before passing it to the model which was achieved by deploying the XGBoost model in conjunction with a JSON parser model in a SageMaker PipelineModel. Some boilerplate code for this implementation can be found here.

In the future I plan to add some more functionalities to this implementation, such as unit tests. So if this is something that might be useful for you, stay tuned for upcoming blog posts!