Authors :
P. NIKHILESH; G. PRABHU RAJ; G. VARUN KUMAR; D. Yoshitha
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/mt9mtkp2
DOI :
https://doi.org/10.5281/zenodo.8046354
Abstract :
Credit card fraud has become a major
worry for both banks and their clients in recent years.
As a result, there is an increasing demand for robust
fraud detection techniques that can detect forged
transactions in real time. The random forest algorithm
is a prominent machine learning technique that has
shown promising results in a variety of classification
problems, including the detection of credit card fraud.
The algorithm is trained on an extensive set of credit
card transactions that includes both illegal and non-
fraudulent transactions. The system's performance is
measured using multiple metrics like as precision,
recall, precision, accuracy, and F1-score. The results of
the experiment show that the proposed system detects
forged transactions with high accuracy and low false
positive rates. The proposed solution can help financial
institutions safeguard their consumers from credit card
theft and save financial damages.
Keywords :
fraud detection techniques, Random Forest Algorithm, fraudulent Transactions, Fraud Detection System,Financial Damages
Credit card fraud has become a major
worry for both banks and their clients in recent years.
As a result, there is an increasing demand for robust
fraud detection techniques that can detect forged
transactions in real time. The random forest algorithm
is a prominent machine learning technique that has
shown promising results in a variety of classification
problems, including the detection of credit card fraud.
The algorithm is trained on an extensive set of credit
card transactions that includes both illegal and non-
fraudulent transactions. The system's performance is
measured using multiple metrics like as precision,
recall, precision, accuracy, and F1-score. The results of
the experiment show that the proposed system detects
forged transactions with high accuracy and low false
positive rates. The proposed solution can help financial
institutions safeguard their consumers from credit card
theft and save financial damages.
Keywords :
fraud detection techniques, Random Forest Algorithm, fraudulent Transactions, Fraud Detection System,Financial Damages
