Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks

By Bénard Wiese, Christian Walter Peter Omlin. 2009

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liyun He-Guelton
2. Sylvie Calabretto
3. Konstantin Ziegler
4. Michael Granitzer
5. Johannes Jurgovsky
6. Pierre-Edouard Portier
7. Narek Abroyan
8. Olivier Caelen

In Management Science and Operations Research

1. Michael Granitzer
2. Narek Abroyan
3. Olivier Caelen

Citations in the corpus

• Neural Networks for Financial Market Risk Classification (2017, methodology): “On the other hand, by doing similar experiment with LSTMs as in [8], we get a value of F1 score, which is even better than our previous experimental result with convolutional networks.”
• Applications of Artificial Intelligence in Machine Learning: Review and Prospect (2015, unknown): “[12]”
• Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, background): “LSTM models also have been explored to recognize fraudulent card transaction [17].”
• Sequence classification for credit-card fraud detection (2018, background): “Aside from [Wiese and Omlin, 2009], sequence classification seems to be a yet unexplored terrain in the credit card fraud detection community.”