Authors :
Dhanashree Diwase; Janhavi Warkari; Abhishek Gawali; Swati Shamkuwar
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/3pa7z2vm
Scribd :
https://tinyurl.com/yesaxys5
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR939
Abstract :
Globally, credit card fraud is a serious threat
to people, businesses, and financial institutions. With the
rise of online transactions, fraudsters have developed
clever ways to take advantage of loopholes in payment
systems. Traditional fraud detection methods based on
manual inspections and rules-based systems are unable
to counteract this new and evolving risk. As a result, the
use of data analytics and machine learning has become a
viable option for real-time detection and prevention of
credit card fraud. The paper looks at using machine
learning algorithms such as logistic regression, decision
trees, random forests, neural networks, etc. to detect
fraudulent transactions We go over the importance of
data sources and components, analytical metrics, and
how fraud detection on the effectiveness of examples. In
addition, we list the current challenges and directions in
which credit card fraud detection is likely to continue,
including the use of blockchain technology and
sophisticated AI techniques. Overall, this study
highlights the importance of credit card theft detection
and the promise of machine learning in mitigating this
ubiquitous problem financial institutions use advanced
machine learning algorithms and analytics function to
detect fraudulent behaviour, protect customer interests,
and maintain payment environment integrity to improve
their capabilities.
Keywords :
Credit Card Fraud Detection, Machine Learning, Deep Learning, Anomaly Detection, Performance Metrics.
Globally, credit card fraud is a serious threat
to people, businesses, and financial institutions. With the
rise of online transactions, fraudsters have developed
clever ways to take advantage of loopholes in payment
systems. Traditional fraud detection methods based on
manual inspections and rules-based systems are unable
to counteract this new and evolving risk. As a result, the
use of data analytics and machine learning has become a
viable option for real-time detection and prevention of
credit card fraud. The paper looks at using machine
learning algorithms such as logistic regression, decision
trees, random forests, neural networks, etc. to detect
fraudulent transactions We go over the importance of
data sources and components, analytical metrics, and
how fraud detection on the effectiveness of examples. In
addition, we list the current challenges and directions in
which credit card fraud detection is likely to continue,
including the use of blockchain technology and
sophisticated AI techniques. Overall, this study
highlights the importance of credit card theft detection
and the promise of machine learning in mitigating this
ubiquitous problem financial institutions use advanced
machine learning algorithms and analytics function to
detect fraudulent behaviour, protect customer interests,
and maintain payment environment integrity to improve
their capabilities.
Keywords :
Credit Card Fraud Detection, Machine Learning, Deep Learning, Anomaly Detection, Performance Metrics.
