Temporal Transaction Scraping Assisted Point of Compromise Detection With Autoencoder Based Feature Engineering
Credit card fraudsters exploit various methods to capture card information. One of the common methods is to duplicate the credit cards by skimming. In this study, we introduce a new point of compromise detection method in order to trace and identify merchants where the skimming operation took place...
Main Authors: | Fuat Ogme, A. Gokhan Yavuz, M. Amac Guvensan, M. Elif Karsligil |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9502728/ |
Similar Items
-
A Closer Look Into the Characteristics of Fraudulent Card Transactions
by: Baris Can, et al.
Published: (2020-01-01) -
On the Black-Box Challenge for Fraud Detection Using Machine Learning (II): Nonlinear Analysis through Interpretable Autoencoders
by: Chaquet-Ulldemolins, J., et al.
Published: (2022) -
Handling Imbalanced Data Classification With Variational Autoencoding And Random Under-Sampling Boosting
by: Ludvigsen, Jesper
Published: (2020) -
Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
by: Igor Mekterović, et al.
Published: (2021-07-01) -
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions
by: Andrey Filchenkov, et al.
Published: (2021-03-01)