End-to-end neural network architecture for fraud scoring in card payments

By J. A. Gómez, Juan Arévalo, Roberto Paredes Palacios, Jordi Nin. 2018

70 citations recorded by the source corpus.

A completely new solution for fraud scoring in card payments validated by one of the biggest bank in the world.Experiments were performed with real dataset with more than 900 millions of credit card operations.Experiments performance is comparable to state of the art solutions but without any human intervention. Millions of euros are lost every year due to fraudulent card transactions. The design and implementation of efficient fraud detection methods is mandatory to minimize such losses. In this paper, we present a neural network based system for fraud detection in banking systems. We use a real world dataset, and describe an end-to-end solution from the practitioners perspective, by focusing on the following crucial aspects: unbalancedness, data processing and cost metric evaluation. Our analysis shows that the proposed solution achieves comparable performance values with state-of-the-art proprietary and costly solutions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu

In Computer Vision and Pattern Recognition

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[115] used LSTMmodel for the credit card fraud detection, whereas in [116], the authors implemented MLP networks to classify if a credit card transaction was fraudulent or not.”