  eCommerce transaction frauds keep changing rapidly. This is the major issue
that prevents eCommerce merchants having a robust machine learning model for
fraudulent transactions detection. The root cause of this problem is that rapid
changing fraud patterns alters underlying data generating system and causes the
performance deterioration for machine learning models. This phenomenon in
statistical modeling is called "Concept Drift". To overcome this issue, we
propose an approach which adds dynamic risk features as model inputs. Dynamic
risk features are a set of features built on entity profile with fraud
feedback. They are introduced to quantify the fluctuation of probability
distribution of risk features from certain entity profile caused by concept
drift. In this paper, we also illustrate why this strategy can successfully
handle the effect of concept drift under statistical learning framework. We
also validate our approach on multiple businesses in production and have
verified that the proposed dynamic model has a superior ROC curve than a static
model built on the same data and training parameters.
