Credit Card Fraud Detection - A Machine Learning Perspective
Shahana Fathima, Leena C. Sekhar, K U Jaseena · International Journal of Science and Research (IJSR) · 2023
The increasing prevalence of credit card fraud in today's digital economy poses a significant challenge to financial institutions and consumers alike.To combat this threat, there is a growing need for robust and efficient fraud detection systems.This paper presents a comprehensive machine learning approach for credit card fraud detection, leveraging advanced techniques and models to enhance the accuracy and reliability of fraud detection mechanisms.Our methodology encompasses data pre -processing, feature engineering, and model selection to construct a highly effective fraud detection pipeline.We explore various machine learning algorithms, including K Nearest Neighbour, Support Vector Machine, Random Forest, Decision Trees and Artificial Neural Networks, to build a predictive model that can distinguish between legitimate and fraudulent credit card transactions.The dataset used for training and evaluation is sourced from historical credit card transaction records, encompassing a wide range of transaction attributes and labels for fraudulent and non -fraudulent activities.We apply rigorous performance metrics, such as precision, recall, F1 -score, to assess the models' efficacy.The proposed model achieves high accuracy rates while minimizing false positives, thus enhancing the overall security of credit card transactions.