English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the increase of credit card usage, the volume of credit card misuse alsohas significantly increased. As a result, financial organizations are workinghard on developing and deploying credit card fraud detection methods, in orderto adapt to ever-evolving, increasingly sophisticated defrauding strategies andidentifying illicit transactions as quickly as possible to protect themselvesand their customers. Compounding on the complex nature of such adversestrategies, credit card fraudulent activities are rare events compared to thenumber of legitimate transactions. Hence, the challenge to develop frauddetection that are accurate and efficient is substantially intensified and, asa consequence, credit card fraud detection has lately become a very active areaof research. In this work, we provide a survey of current techniques mostrelevant to the problem of credit card fraud detection. We carry out our surveyin two main parts. In the first part,we focus on studies utilizing classicalmachine learning models, which mostly employ traditional transnational featuresto make fraud predictions. These models typically rely on some static physicalcharacteristics, such as what the user knows (knowledge-based method), or whathe/she has access to (object-based method). In the second part of our survey,we review more advanced techniques of user authentication, which use behavioralbiometrics to identify an individual based on his/her unique behavior whilehe/she is interacting with his/her electronic devices. These approaches rely onhow people behave (instead of what they do), which cannot be easily forged. Byproviding an overview of current approaches and the results reported in theliterature, this survey aims to drive the future research agenda for thecommunity in order to develop more accurate, reliable and scalable models ofcredit card fraud detection.

A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection