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Computer Science
Machine Learning

Credit Card Fraud Detection using Machine Learning Methods

Profile image of IJRASET PublicationIJRASET Publication

2020, international journal for research in applied science and engineering technology ijraset

https://doi.org/10.22214/IJRASET.2020.6233
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7 pages

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Abstract

A major problem which is affecting growth in financial services is "CREDIT CARD FRAUD". Many organizations lost their amount due to these frauds. Even though many research studies are made on fraud detection, they lack on analyzing data extracted from actual transaction, due to privacy issues. Here, certain machine learning algorithms are used to detect fraudulent transactions. First, the Standard methods are applied. Then, in combination hybrid methods such as AdaBoost and majority voting are applied. A publicly available datasets are used so that model efficacy can be evaluated. Later, a real-world credit card data set is analyzed which is taken from certain financial institution. Further, to estimate the robustness of the algorithm, noise is added to the samples. By comparing various machine learning algorithms, the main aim is to find the best in those to detect the fraudulent transactions to avoid credit card fraud. The experimental results indicate that the hybrid methods such as majority voting efficiently provides nearly best accuracy for detecting fraudulent transactions of Credit cards.

Figures (4)
Figure 4: Accuracy graph of Majority voting Here, we predict the class label y* via majority (plurality) voting of each classifier Cj:
Figure 4: Accuracy graph of Majority voting Here, we predict the class label y* via majority (plurality) voting of each classifier Cj:
Majority voting is a hybrid algorithm is used in data classification. It is used for combining two or more algorithms in order to increase the accuracy of the model, prediction is made by each algorithm for every test sample. The majority of votes is considered ac final ontniut.
Majority voting is a hybrid algorithm is used in data classification. It is used for combining two or more algorithms in order to increase the accuracy of the model, prediction is made by each algorithm for every test sample. The majority of votes is considered ac final ontniut.

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References (10)

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