Results 81 to 90 of about 112,889 (239)
Machine Learning Techniques for Credit Card Fraud Detection [PDF]
, 2020 The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years.
Abd El-Hamid, Hossam Eldin Mohammed, Ahmed Abdou +3 more
core +1 more source
Ecosystem Orchestration Work in the Digital Transformation of Ecosystems
R&D Management, Volume 56, Issue 2, Page 363-384, March 2026.ABSTRACT Developing the right structure of interdependence is crucial to determining orchestrators' success in digitally transforming ecosystems and deepening value creation. Although firms' digitally transforming ecosystems represent a recent yet fast‐growing phenomenon, the current literature offers limited theoretical insights and empirical guidance
Leonardo Augusto de Vasconcelos Gomes +4 more
wiley +1 more source
Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest [PDF]
, 2021 Tzu-Hsuan Lin, Jehn‐Ruey Jiang
openalex +1 more source
Card Defender - Credit Card Fraud Detection System
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: As the world becomes increasingly digitized, online transactions have become an indispensable part of our daily lives. The increased use of credit cards for online purchases has resulted in a growing concern about credit card fraud, both for businesses and consumers.
openaire +1 more source
An Empirical Study of AML Approach for Credit Card Fraud Detection—Financial Transactions [PDF]
, 2020 Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives ...
Jain, Anurag, Singh, Ajeet
core
Engineering Reports, Volume 8, Issue 2, February 2026.In this study, we predicted BNPL loan defaults by introducing two new variables related to the use of digital banking platforms and assessing their impact. We compared ensemble learning models in combination with data balancing methods and logistic regression (for feature selection).
Parivash Khalili +3 more
wiley +1 more source
Robust Model‐Based Semi‐Supervised Clustering of Incomplete Records
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.ABSTRACT This paper develops a multivariate t$$ t $$‐mixture model‐based semi‐supervised clustering methodology for datasets with incomplete records. Specifically, we consider the case where not all features are always observed, as well as the case where label information for some of the records is available, where the interest is in grouping all of ...
Joshua D. Berlinski, Ranjan Maitra
wiley +1 more source
MathematicsThe Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought ...
Boyu Liu, Longrui Wu, Shengdong Mu
doaj +1 more source
The changing nature of U.S. card payment fraud: industry and public policy options [PDF]
As credit and debit card payments have become the primary payment instrument in retail transactions, awareness of identity theft and concerns over the safety of payments has increased. Traditional forms of card payment fraud are still an important threat,
Richard J. Sullivan
core
A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks
MathematicsWith the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques,
Yanxi Wu +3 more
doaj +1 more source