Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms

Sadineni, Praveen Kumar

doi:10.1109/i-smac49090.2020.9243545

Cited by 31 publications

(11 citation statements)

References 10 publications

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“…Random forest algorithm based novel mobile money transaction fraud detection have better accuracy compared to logistic regression algorithm based mobile money transactions fraud detection (Sadineni 2020) et al have implemented random forest and logistic regression algorithms to detect the frauds in credit card systems and obtained accuracy 98% (Sadineni 2020). (Mouawi et al 2018) introduced a new framework to implement logistic regression and random forest algorithms to find the types of frauds, they used the transaction history as an ensemble classifier to detect the frauds and obtained accuracy 93% (Mouawi et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…There are 173 papers published on mobile money fraud detection in sciencedirect and 250 papers on google scholar and 3 papers were published in ieee xplore for fraud detection. Sadineni et al proposed a machine learning algorithm that uses deep learning algorithms to detect the frauds that show the better significance (Sadineni 2020). Mubalaike et al uses deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The methods which are used before have less accuracy and detection rate in frauds. Sadineni et al implemented a new framework to find the frauds manually with the help of deep learning algorithms and achieve less accuracy (Sadineni 2020). The main purpose of this study is to develop an novel mobile money transaction fraud detection using Random forest algorithm and logistic regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The obtained transaction data may not have the same parameters and might be different from column to column. Therefore, the collected transaction history data should be similar, and maintaining consistency is important in terms of making efficient analysis and consistency(Sadineni 2020). There is no opposite finding related to this proposed algorithm.…”

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confidence: 99%

“…G.s. et al 2021;Sadineni 2020)et al have 3000 transaction histories and 7 attributes. This research used LRA and RFA classifiers to achieve accuracy of 93%.…”

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confidence: 99%

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Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

Kumar¹,

Nalini²

2021

revistageintec

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Aim:The main motto of the study is to detect the frauds in mobile money transactions using logistic regression and random forest algorithms and comparing their accuracy. Materials and Methods: Logistic regression (N=10) and random forest algorithm(N=10) was iterated 20 times and detected the frauds. Results and Discussion: Random forest has significantly better accuracy (99.6%) compared to logistic regression (92.6%). The statistical significance of random forest algorithm (p<0.018 Independent sample T-test) is high. Conclusion: Within the limits of this study, random forest algorithm offers better accuracy to detect frauds in mobile money transactions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…G.s. et al 2021;Sadineni 2020)et al have 3000 transaction histories and 7 attributes. This research used LRA and RFA classifiers to achieve accuracy of 93%.…”

mentioning

confidence: 99%

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Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

Kumar¹,

Nalini²

2021

revistageintec

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Artificial Intelligence-Based Digital Financial Fraud Detection

Elyassami

Humaid

Alhosani

et al. 2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Digital frauds get a dramatic increase over the years and lead to considerable losses. Detecting fraudulent attempts is valuable to many industries and especially to the banking and financial sectors. To help in anticipating and accurately identifying whether a transaction is fraudulent, machine learning-based models are the key solution for banking and financial institutions. In this paper, an artificial intelligence-based model was built using deep learning and was trained using stochastic gradient descent and feedforward neural networks. The dropout regularization has been utilized to enhance the generalization capabilities of the digital transaction classification model. Different activation functions were used and explored such as the max-out, the hyperbolic tangent, the rectifier linear unit, and the exponential rectifier linear unit. The impact of the learning rate on the model performance was analyzed. For the evaluation of the model, we did use of different metrics such as the accuracy, the precision, and the recall. The obtained results are promising, and the developed model can be used effectively to defend the banking sector against digital frauds.

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