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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Mangal Varsha,Nandita Avasthi
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Abstract
With the rapid increase in digital transactions, credit card fraud has become a significant concern for financial institutions and consumers alike. Traditional rule-based fraud detection systems often struggle to keep pace with the evolving tactics of fraudsters. Machine learning (ML) techniques offer promising solutions for detecting fraudulent transactions due to their ability to adapt and learn from data patterns. In this paper, we present a comprehensive analysis of various ML algorithms for credit card fraud detection.
Firstly, we review the existing literature on credit card fraud detection methodologies, highlighting the limitations of traditional approaches and the advantages of ML-based techniques. We then discuss the key challenges in building effective fraud detection models, including imbalanced datasets, feature selection, model interpretability, and scalability.
Next, we provide an overview of different ML algorithms commonly used in credit card fraud detection, including supervised learning algorithms such as logistic regression, decision trees, random forests, support vector machines, and ensemble methods like gradient boosting and AdaBoost. We also explore the potential of unsupervised learning techniques, particularly clustering algorithms such as k-means and DBSCAN, and anomaly detection methods like isolation forest and autoencoders.
Furthermore, we present a detailed analysis of feature engineering strategies essential for improving the performance of fraud detection models. Feature engineering techniques such as PCA (Principal Component Analysis), feature scaling, and synthetic minority oversampling technique (SMOTE) are discussed in detail.
To evaluate the effectiveness of different ML algorithms, we conduct experiments on publicly available credit card transaction datasets, including the Kaggle Credit Card Fraud Detection dataset. We compare the performance of various algorithms in terms of metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Additionally, we investigate the interpretability of the models to understand the factors contributing to their decisions.
IJCRT's Publication Details
Unique Identification Number - IJCRT2404474
Paper ID - 255810
Author type - Indian Author
Page Number(s) - e124-e128
Pubished in - Volume 12 | Issue 4 | April 2024
DOI (Digital Object Identifier) - http://doi.one/10.1729/Journal.39020
No Of Downloads - 48
Author Country - India, 302028, Jaipur, Jaipur, 302028, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Mangal Varsha,Nandita Avasthi, "Detecting Credit Card Fraud Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.e124-e128, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT2404474.pdf
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