  Fraud detection is essential in financial services, with the potential of
greatly reducing criminal activities and saving considerable resources for
businesses and customers. We address online fraud detection, which consists of
classifying incoming transactions as either legitimate or fraudulent in
real-time. Modern fraud detection systems consist of a machine learning model
and rules defined by human experts. Often, the rules performance degrades over
time due to concept drift, especially of adversarial nature. Furthermore, they
can be costly to maintain, either because they are computationally expensive or
because they send transactions for manual review. We propose ARMS, an automated
rules management system that evaluates the contribution of individual rules and
optimizes the set of active rules using heuristic search and a user-defined
loss-function. It complies with critical domain-specific requirements, such as
handling different actions (e.g., accept, alert, and decline), priorities,
blacklists, and large datasets (i.e., hundreds of rules and millions of
transactions). We use ARMS to optimize the rule-based systems of two real-world
clients. Results show that it can maintain the original systems' performance
(e.g., recall, or false-positive rate) using only a fraction of the original
rules (~ 50% in one case, and ~ 20% in the other).
