Analysis of various machine learning models in detecting credit card fraud activities

Md Liakat Ali; Akshara Reddy Ampojwala; Dhanush Reddy Sudugu; John Marousis; Taahir Guerrier; Mourya Reddy Narasareddygari

doi:10.1117/12.3024128

27 May 2024 Analysis of various machine learning models in detecting credit card fraud activities

Md Liakat Ali, Akshara Reddy Ampojwala, Dhanush Reddy Sudugu, John Marousis, Taahir Guerrier, Mourya Reddy Narasareddygari

Author Affiliations +

Proceedings Volume 13169, Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024); 131690D (2024) https://doi.org/10.1117/12.3024128
Event: Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024), 2024, Bangkok, Thailand

Abstract

Credit card fraud has been increasing with the rise of cashless payments, making it difficult to identify fraudulent transactions among thousands of normal ones. Machine learning algorithms can help address this issue by classifying transactions as either fraud or non-fraud in a dataset. For this project, we utilized a highly imbalanced dataset from Kaggle containing European cardholder data. While the dataset is mostly clean, we needed to balance it for training and testing purposes through undersampling. We developed six classification models to differentiate between fraudulent and non-fraudulent transactions and evaluated their performance based on accuracy and F1 score. The KNN model outperforms the other models for the dataset we used in our experiment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Md Liakat Ali, Akshara Reddy Ampojwala, Dhanush Reddy Sudugu, John Marousis, Taahir Guerrier, and Mourya Reddy Narasareddygari "Analysis of various machine learning models in detecting credit card fraud activities", Proc. SPIE 13169, Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024), 131690D (27 May 2024); https://doi.org/10.1117/12.3024128

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