Feature engineering strategies for credit card fraud detection

By Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Björn E. Ottersten. Expert systems with applications, 2016

419 citations recorded by the source corpus.

Credit card fraud detection evaluation measure.Each example is assumed to have different financial cost.Transaction aggregation strategy for predicting fraud.Periodic features using the von Mises distribution.Code is open source and available at albahnsen.com/CostSensitiveClassification. Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the fraud detection process. Moreover, when constructing a credit card fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real credit card fraud dataset provided by a large European card processing company, we compare state-of-the-art credit card fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.

Subfields: Accounting, Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/33bb63d6c3145dd85c5f897128cc70d0e1ba3d38

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Citations in the corpus

Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, background): “The successful result of using machine learning to solve classification problems in many domains have concerned many researchers to use these models to recognize fraudulent transactions [3].”
Sequence classification for credit-card fraud detection (2018, methodology): “To construct such feature aggregations, we follow the procedure that was recently proposed by [Bahnsen et al., 2016].”
Deep learning detecting fraud in credit card transactions (2018, background): “The creation of domain expertise features has proven to improve the predictive accuracy of models in detecting credit card fraud [25].”