Data engineering for fraud detection

Bart Baesens, Sebastiaan Höppner, Tim Verdonck

Abstract: Highlights•Companies increasingly rely upon data-driven methods for detecting fraud.•Data engineering is of utmost importance to improve the performance of most machine learning models.•Our data engineering process is decomposed into several feature and instance engineering steps.•The benefits of data engineering is illustrated on a payment transactions data set from a large European Bank.