Credit Card Fraud Detection Using Machine Learning

Sailusha, Ruttala; Gnaneswar, V.; Ramesh, Raj; Rao, G. Ramakoteswara

doi:10.1109/iciccs48265.2020.9121114

Cited by 108 publications

(32 citation statements)

References 5 publications

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“…The predictions are transformed using the logistic function, which returns probability values between 0 and 1. Logistic regression is widely applied for classification tasks [17] and widely adapted for Industry 4.0 because it is easy to implement, has good accuracy, and is very efficient to train. Itoo et al [5] indicated that logistic regression achieved the best performance in fraud detection compared to Naï ve Bayes and KNN.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Digital payment fraud detection methods in digital ages and Industry 4.0

Chang

Doan

Stefano

et al. 2022

Computers and Electrical Engineering

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

Digital payment fraud detection methods in digital ages and Industry 4.0

Chang

Doan

Stefano

et al. 2022

Computers and Electrical Engineering

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“…Sailusha R et al employed random forest and Adaboost algorithm to detect fraudulent transactions [8]. Performance was analysed by accuracy and F1-score.…”

Section: Related Workmentioning

confidence: 99%

Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Mahesh

Afrouz

Areeckal

2022

J. Phys.: Conf. Ser.

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Every year there is an increasing loss of a huge amount of money due to fraudulent credit card transactions. Recently there is a focus on using machine learning algorithms to identify fraud transactions. The number of fraud cases to non-fraud transactions is very low. This creates a skewed or unbalanced data, which poses a challenge to training the machine learning models. The availability of a public dataset for this research problem is scarce. The dataset used for this work is obtained from Kaggle. In this paper, we explore different sampling techniques such as under-sampling, Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek, to work on the unbalanced data. Classification models, such as k-Nearest Neighbour (KNN), logistic regression, random forest and Support Vector Machine (SVM), are trained on the sampled data to detect fraudulent credit card transactions. The performance of the various machine learning approaches are evaluated for its precision, recall and F1-score. The classification results obtained is promising and can be used for credit card fraud detection.

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“…Sailusha et. al., [12] compares random forest and adaboost algorithms as machine learning techniques for credit card fraud detection. Both the algorithms have same accuracy but when precision, recall and F1 scores are considered, the random forest algorithm has the highest value than adaboost algorithm.…”

Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection using Supervised and Unsupervised Learning

Gowda¹

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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In the present monetary situation, credit card use has gotten normal. These cards allow the user to make payments online and even in person. Online payments are very convenient, but it comes with its own risk of fraud. With the expanding number of credit card users, frauds are also expanding at the same rate. Some machine learning algorithms can be applied to tackle this problem. In this paper an evaluation of supervised and unsupervised machine learning algorithms has been presented for credit card fraud detection.

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Credit Card Fraud Detection Using Machine Learning

Cited by 108 publications

References 5 publications

Digital payment fraud detection methods in digital ages and Industry 4.0

Digital payment fraud detection methods in digital ages and Industry 4.0

Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Credit Card Fraud Detection using Supervised and Unsupervised Learning

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