Solving the "false positives" problem in fraud prediction
This addresses a critical issue for the fraud prediction industry, where false positives lead to customer inconvenience and financial losses, though it is an incremental improvement using existing methods.
The paper tackled the problem of high false positives in fraud prediction by using automated feature engineering and a random forest classifier, achieving a 54% reduction in false positives and saving 190K euros on a dataset of 1.852 million transactions.
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.