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eCommerce transaction frauds keep changing rapidly. This is the major issuethat prevents eCommerce merchants having a robust machine learning model forfraudulent transactions detection. The root cause of this problem is that rapidchanging fraud patterns alters underlying data generating system and causes theperformance deterioration for machine learning models. This phenomenon instatistical modeling is called "Concept Drift". To overcome this issue, wepropose an approach which adds dynamic risk features as model inputs. Dynamicrisk features are a set of features built on entity profile with fraudfeedback. They are introduced to quantify the fluctuation of probabilitydistribution of risk features from certain entity profile caused by conceptdrift. In this paper, we also illustrate why this strategy can successfullyhandle the effect of concept drift under statistical learning framework. Wealso validate our approach on multiple businesses in production and haveverified that the proposed dynamic model has a superior ROC curve than a staticmodel built on the same data and training parameters.

Adaptive Fraud Detection System Using Dynamic Risk Features