Credit card fraud detection based on machine learning
Các tác giả
Từ khóa:
SMOTE, machine learning, classification, imbalanced data, fraud detectionTóm tắt
Online transactions have increased dramatically over the decades. Credit card transactions account for a large proportion of these transactions. This leads to an increase in credit card fraud transactions, causing damage to the financial industry. Therefore, it is important to create fraud detection systems, consisting of two labels fraud and no fraud. However, the dataset is not balanced between the two labels. In this paper, we use the resampling method such as SMOTE to process this unbalanced dataset to obtain a balanced dataset. The machine learning (ML) algorithms, named random forest, k nearest neighbors, decision tree, and logistic regression are applied to this balanced dataset to create ML models. The performance of these ML models is evaluated through accuracy, recall, precision, and F1 score. We observed that the SMOTE-based random forest algorithm identifies frauds in a better way than other algorithms.
Abstract
Online transactions have increased dramatically over the decades. Credit card transactions account for a large proportion of these transactions. This leads to an increase in credit card fraud transactions, causing damage to the financial industry. Therefore, it is important to create fraud detection systems, consisting of two labels fraud and no fraud. However, the dataset is not balanced between the two labels. In this paper, we use the resampling method such as SMOTE to process this unbalanced dataset to obtain a balanced dataset. The machine learning (ML) algorithms, named random forest, k nearest neighbors, decision tree, and logistic regression are applied to this balanced dataset to create ML models. The performance of these ML models is evaluated through accuracy, recall, precision, and F1 score. We observed that the SMOTE-based random forest algorithm identifies frauds in a better way than other algorithms.
Tài liệu tham khảo
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