Fraud Detection


Fraud comes in many forms - loan application, workers compensation, payroll, identity theft, Ponzi-schemes, insider trading, and securities - not detecting it early can have dire consequences resulting in penalties, both legal and monetary. Machine learning is well suited to detecting fraud due to its ability to mine large amounts of data quickly, uncovering hidden patterns that might not otherwise have been identified.

Auger analyzes all available algorithms to better identify the most effective one, including:

  • Neural Networks

  • Decision Tree

  • Random Forest

  • Logistic Regression

  • Linear Regression

  • Linear Support Vector Machine

  • Gradient and ADA Boosting

  • Naive Bayes classifiers

Additionally, it automatically tunes hyperparamaters to make the process faster and easier. Auger also provides ensemble generation, a powerful technique which aims to increase the predictive performance for a given machine learning task by combining several predictive models. All of these features combined identify fraud faster and easier.



Sign up and get free computing time!

  1. Upload your csv

  2. Train your models in parallel to find the best performer

  3. Deploy a prediction endpoint to get real time predictions