Authors :
Katta Veera Venkata Surya Teja; Kamana Vijay Vamsi; Kunadharaju Vinod Varma; Kandikatla Sandeep
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/y97yajmd
Scribd :
https://tinyurl.com/r8e53uc7
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR388
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Credit card fraud is an easy target. E-
commerce and many other online sites collect money
online, increasing the risk of online fraud. As fraud
increases, researchers have begun using different
learning techniques to detect and analyze fraud in online
businesses. The main goal of this article is to design and
develop a new streaming data transfer fraud method
that aims to identify customer context and extract
behavior from the business. Card holders are divided
into different groups according to transaction fees.
Sliding windows are then used to combine transactions
from different cardholder groups, allowing the behavior
of each group to be separated. The different groups were
then divided into classes for training. Classes with better
scores can be selected as the best way to predict fraud.
Therefore, the following feedback strategy is adopted to
solve the drift law problem. In this article, we use the
European credit card fraud dataset.
Keywords :
Credit Card Fraud Detection; Machine Learning Algorithms; Vague Search; See Instructions; Unsupervised Learning; Kev Faib Algorithm.
Credit card fraud is an easy target. E-
commerce and many other online sites collect money
online, increasing the risk of online fraud. As fraud
increases, researchers have begun using different
learning techniques to detect and analyze fraud in online
businesses. The main goal of this article is to design and
develop a new streaming data transfer fraud method
that aims to identify customer context and extract
behavior from the business. Card holders are divided
into different groups according to transaction fees.
Sliding windows are then used to combine transactions
from different cardholder groups, allowing the behavior
of each group to be separated. The different groups were
then divided into classes for training. Classes with better
scores can be selected as the best way to predict fraud.
Therefore, the following feedback strategy is adopted to
solve the drift law problem. In this article, we use the
European credit card fraud dataset.
Keywords :
Credit Card Fraud Detection; Machine Learning Algorithms; Vague Search; See Instructions; Unsupervised Learning; Kev Faib Algorithm.
