A novel GBT-based approach for cross-channel fraud detection on real-world banking transactions

Name: A novel GBT-based approach for cross-channel fraud detection on real-world banking transactions
Author: Dolu, U., Sefer, Emre

İsim	A novel GBT-based approach for cross-channel fraud detection on real-world banking transactions
Yazar	Dolu, U., Sefer, Emre
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Concept drift, Cross channel fraud, Gradient boosting tree, Imbalanced data
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303108332-7
Kayıt Numarası	b7089b74-0ea6-4791-a35e-1e7027aae467
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	The most recent research on hundreds of financial institutions uncovered that only 26 % of them have a team assigned to detect cross-channel fraud. Due to the developing technologies, various fraud techniques have emerged and increased in digital environments. Fraud directly affects customer satisfaction. For instance, only in the UK, the total loss of fraud transactions was £1.26 billion in 2020. In this paper, we come up with a Gradient Boosting Tree (GBT)-based approach to efficiently detect cross-channel frauds. As part of our proposed approach, we also figured out a solution to generate training sets from imbalanced data, which also suffers from concept drift problems due to changing customer behaviors. We boost the performance of our GBT model by integrating additional demographic, economic, and behavioral features as a part of feature engineering. We evaluate the performance of our cross-channel fraud detection method on a real banking dataset which is highly imbalanced in terms of frauds which is another challenge in the fraud detection problem. We use our trained model to score real-time cross-channel transactions by a leading private bank in Turkey. As a result, our approach can catch almost 75 % of total fraud loss in a month with a low false-positive rate.
Editör	Maglogiannis, I., Iliadis, L., Macintyre, J.
DOI	10.1007/978-3-031-08333-4_6
Cilt	646 IFIP

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A novel GBT-based approach for cross-channel fraud detection on real-world banking transactions

Yazar Dolu, U., Sefer, Emre

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Concept drift, Cross channel fraud, Gradient boosting tree, Imbalanced data

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303108332-7

Kayıt Numarası b7089b74-0ea6-4791-a35e-1e7027aae467

Lokasyon Computer Science

Tarih 2022

Örnek Metin The most recent research on hundreds of financial institutions uncovered that only 26 % of them have a team assigned to detect cross-channel fraud. Due to the developing technologies, various fraud techniques have emerged and increased in digital environments. Fraud directly affects customer satisfaction. For instance, only in the UK, the total loss of fraud transactions was £1.26 billion in 2020. In this paper, we come up with a Gradient Boosting Tree (GBT)-based approach to efficiently detect cross-channel frauds. As part of our proposed approach, we also figured out a solution to generate training sets from imbalanced data, which also suffers from concept drift problems due to changing customer behaviors. We boost the performance of our GBT model by integrating additional demographic, economic, and behavioral features as a part of feature engineering. We evaluate the performance of our cross-channel fraud detection method on a real banking dataset which is highly imbalanced in terms of frauds which is another challenge in the fraud detection problem. We use our trained model to score real-time cross-channel transactions by a leading private bank in Turkey. As a result, our approach can catch almost 75 % of total fraud loss in a month with a low false-positive rate.

Editör Maglogiannis, I., Iliadis, L., Macintyre, J.

DOI 10.1007/978-3-031-08333-4_6

Cilt 646 IFIP