Improving Credit Card Fraud Detection Using Machine Learning with Under-Samplingand SMOTE Techniques

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

XDP-ML: A Game-Changer in Intrusion Detection Systems for Modern Cybersecurity

Intrusion Detection system (IDS) plays a vital role in cyber security. Traditional approaches are not good enough to detect properly the large threats. Machine learning provides a promising solution and good accuracy by...

Gemstones Supply Chain Management throughBlockchain Mechanism

T provenance of gemstones significantly enhances their value. However, both conventional supply chain management and digital systems are susceptible to counterfeiting, loss, and theft. Blockchain has emerged as a suita...

Python-Based Land Suitability Analysis for Wheat Cultivation Using MCEand Google Earth Engine in Punjab-Pakistan

The present study aims to examine the suitability of wheat crops in the four districts of Sheikhupura, Gujranwala, Hafizabad, and Nankana Sahib by conducting a thorough examination of various environmental parameters....

Computational Analysisof ModelHousesof Da Kali KORin Matta Swat

Natural disasters such as floods and earthquakes, exacerbated by global warming and environmental degradation, pose significant challenges for modern architecture. This study critically evaluates a rural residential ho...

IoTin Developing the Smart Farming and Agricultural Technologies

Background: The Internet of Things (IoT) is streamlining processes in food and agriculture, especially in developing countries with agriculture-based economies. These countries stand to gain a lot from the IoT innovati...

Download PDF file
  • EP ID EP760542
  • DOI -
  • Views 18
  • Downloads 0

How To Cite

Muhammad Talha Jahangir, Nauman Khursheed, Usama (2024). Improving Credit Card Fraud Detection Using Machine Learning with Under-Samplingand SMOTE Techniques. International Journal of Innovations in Science and Technology, 6(4), -. https://europub.co.uk/articles/-A-760542