Improving Credit Card Fraud Detection Using Machine Learning with Under-Samplingand SMOTE Techniques
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Credit card fraud detection is currently the most popular implementation domain of Computational Intelligence techniques. A common issue in the present world is being faced by many organizations and institutions. This is due to the increase in the frequency of transactions, which are now conducted electronically and a higher increase in the number of electronic commerce platforms. In the present world, we are experiencing many credit card issues. In this paper, we apply various algorithms of machine learning as random forest, logistic regression and k-Nearest Neighbors (KNN) to train the specified machine learning model using a given dataset to design the comparative conducted on the accuracy and various measures of the models as it is being implemented via each of such algorithms. To address this, we evaluate the possibility of under-sampling and SMOTE as approaches that can enhance multiple machine-learning models. An accuracy of 99.99% in the dataset was achieved using the SMOTE technique with the Random Forest model. This research concludes that SMOTE improves the performance of the machine learning model for fraud identification and presents a more efficient approach to address the problem of class imbalance.
Authors and Affiliations
Muhammad Talha Jahangir, Nauman Khursheed, Usama
Keywords
Related Articles
- EP ID EP760542
- DOI -
- Views 22
- Downloads 0
Advanced Blast Algorithm for Molecular Identification, Biodegradation and Decolorization of Synthetic Melanoidins Using Fungal Species Isolated from Soil and Spent Wash
Introduction/Importance of Study: Distillery spent wash contains a high organic load as Melanoidins. It is generated due to the Millard reaction, which produces sugar and amino acids, leading to extensive water and soi...
Image Compression Exploration using Discrete Wavelets Transform Families and Level
This analysis paper is based on Discrete Wavelets Transform (DWT) for image compression using wavelets families and levels. The DW transforms the image or data into frequency components that match its resolution scale...
Advanced AI Mechanics in Unity 3D for Immersive Gameplay.A Study on Finite State Machines & Artificial Intelligence
his research explores the history and operationalization of cutting-edge AI technologies, developed for the Unity 3D video engine, in particular how Artificial Intelligence (AI), animation, and FSMs have been used in v...
https://journal.50sea.com/index.php/IJIST/article/view/1078/1629
The cryptocurrency market has evolved in unprecedented ways over the past decade. However, due to the high price volatility associated with cryptocurrencies, predicting their prices remains an attractive research topic...
Optimizing Human Activity Recognition with Ensemble Deep Learning on Wearable Sensor Data
In recent years, the research community has shown a growing interest in the continuous temporal data gathered from motion sensors integrated into wearable devices. This type of data is highly valuable for analyzing hum...