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:
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.