  In this paper, we present an automated feature engineering based approach to
dramatically reduce false positives in fraud prediction. False positives plague
the fraud prediction industry. It is estimated that only 1 in 5 declared as
fraud are actually fraud and roughly 1 in every 6 customers have had a valid
transaction declined in the past year. To address this problem, we use the Deep
Feature Synthesis algorithm to automatically derive behavioral features based
on the historical data of the card associated with a transaction. We generate
237 features (>100 behavioral patterns) for each transaction, and use a random
forest to learn a classifier. We tested our machine learning model on data from
a large multinational bank and compared it to their existing solution. On an
unseen data of 1.852 million transactions, we were able to reduce the false
positives by 54% and provide a savings of 190K euros. We also assess how to
deploy this solution, and whether it necessitates streaming computation for
real time scoring. We found that our solution can maintain similar benefits
even when historical features are computed once every 7 days.
