  Detecting concept drift is a well known problem that affects production
systems. However, two important issues that are frequently not addressed in the
literature are 1) the detection of drift when the labels are not immediately
available; and 2) the automatic generation of explanations to identify possible
causes for the drift. For example, a fraud detection model in online payments
could show a drift due to a hot sale item (with an increase in false positives)
or due to a true fraud attack (with an increase in false negatives) before
labels are available. In this paper we propose SAMM, an automatic model
monitoring system for data streams. SAMM detects concept drift using a time and
space efficient unsupervised streaming algorithm and it generates alarm reports
with a summary of the events and features that are important to explain it.
SAMM was evaluated in five real world fraud detection datasets, each spanning
periods up to eight months and totaling more than 22 million online
transactions. We evaluated SAMM using human feedback from domain experts, by
sending them 100 reports generated by the system. Our results show that SAMM is
able to detect anomalous events in a model life cycle that are considered
useful by the domain experts. Given these results, SAMM will be rolled out in a
next version of Feedzai's Fraud Detection solution.
