Applying Machine Learning Techniques to Detect Credit Card Fraud

Prof. Gulafsha  Anjum; Prof. Nidhi  Pateriya; Prof. Neha  Thakre; Aayush  Tiwari; Shivanjay  Mahanoor

doi:10.15680/IJIRCCE.2023.1106074

Prof. Gulafsha Anjum Department of Computer Science & Engineering, Baderia Global Institute of Engineering & Management, Jabalpur Madhya Pradesh, India.
Prof. Nidhi Pateriya Department of Computer Science & Engineering, Baderia Global Institute of Engineering & Management, Jabalpur Madhya Pradesh, India.
Prof. Neha Thakre Department of Computer Science & Engineering, Baderia Global Institute of Engineering & Management, Jabalpur Madhya Pradesh, India.
Aayush Tiwari Department of Computer Science & Engineering, Baderia Global Institute of Engineering & Management, Jabalpur Madhya Pradesh, India.
Shivanjay Mahanoor Department of Computer Science & Engineering, Baderia Global Institute of Engineering & Management, Jabalpur Madhya Pradesh, India.

DOI: https://doi.org/10.15680/IJIRCCE.2023.1106074

Keywords: Credit Card Fraud Detection, Machine Learning, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Imbalanced Data.

Abstract

Credit card fraud presents a major challenge to financial institutions, resulting in significant economic losses each year. This paper investigates the use of machine learning techniques to detect fraudulent credit card transactions. By analyzing a publicly available dataset, we evaluate the performance of various algorithms, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. The objective is to identify the most effective model for accurately detecting fraudulent activities while minimizing false positives. Additionally, the paper addresses the challenges of dealing with imbalanced datasets and the strategies employed to overcome these issues.