Credit Card Fraud Detection Based on Machine and Deep Learning

Najadat, Hassan; Altiti, Ola; Aqouleh, Ayah Abu; Younes, Mutaz

doi:10.1109/icics49469.2020.239524

Cited by 76 publications

(26 citation statements)

References 8 publications

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“…A checklist is provided in S2 Table . The results show that deep learning methods are more powerful than the traditional machine learning and statistical approaches although they haven't been fully employed. Also, the conclusion that ensembles of several methods outperform a single one has been proved in some of the related researches [9,11,75,81,103,104].…”

Section: Discussionmentioning

confidence: 91%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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Section: Discussionmentioning

confidence: 91%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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“…Najadat et al [21] applied BiLSTM-MaxPooling-BiGRU-216 MaxPooling to predict fraud. The authors also applied a naive 217 base, voting, AdaBoost, random forests, decision tree, and 218 logistic regression to compare the effects of each model.…”

Section: B Deep Learning -Based Approachmentioning

confidence: 99%

A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network

et al. 2022

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The rapid development of technology has digitized customer payment behavior towards a cashless society. To a certain extent, this has created a feast for miscreants to commit fraud. According to Nilson (2020), global fraud loss is projected to reach over $35 billion by 2025. Consequently, the need for a novel method to prevent this menace is undisputed. This research was conducted on the IEEE-CIS Fraud Detection Dataset provided by Vesta Corporation. Based on the logic of labeling for converting the entire account to ''Fraud=1'' once the credit card has fraud, we navigate the research process towards predicting fraudulent credit cards rather than fraudulent transactions. The key idea behind the proposed model is user separation, in which we divide users into old and new people before applying CatBoost and Deep Neural Network to each category, respectively. In addition, a variety of techniques to improve detection accuracy, namely handling heavily imbalanced datasets, feature transformation, and feature engineering, are also presented in detail in this paper. The experimental results showed that our model performed well, as we obtained AUC scores of 0.97 (CatBoost) and 0.84 (Deep Neural Network).

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“…Set data fraud detection yang tidak seimbang dapat diseimbangkan menggunakan metode undersampling, oversampling dan SMOTE. Hasil pengklasifikasi mesin menunjukkan hasil yang lebih baik dengan area under curve (AUC) sebesar 91,37% dihasilkan dengan metode oversampling [14].…”

Section: Gambar 1 Ilustrasi Lapisan Neuron Dalam Deep Learningunclassified

Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik

Zamachsari

Puspitasari

2021

RESTI

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The rapid development of information technology coupled with an increase in public activity in electronic financial transactions has provided convenience but has been accompanied by the occurrence of fraudulent financial transactions. The purpose of this research is how to find the best model to be implemented in the banking payment system in detecting fraudulent electronic financial transactions so as to prevent losses for customers and banks. Fraud detection uses machine learning with ensemble and deep learning with SMOTE. Financial transaction data is taken from bank payment simulations built with the concept of Multi Agent-Based Simulation (MABS) by banks in Spain. To build the best model, not only pay attention to the accuracy value, but the value of precision is a value that needs attention. A precision score is very important for fraud prevention. Fraud detection gets the best results without the SMOTE process by using deep learning with an accuracy score of 99.602% and precision score of 90.574%. By adding SMOTE, it will increase the accuracy score and precision score with the best model produced in the Extra Trees Classification with an accuracy score of 99.835% and precision score of 99.786%.

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Credit Card Fraud Detection Based on Machine and Deep Learning

Cited by 76 publications

References 8 publications

Machine learning-driven credit risk: a systemic review

Machine learning-driven credit risk: a systemic review

A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network

Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik

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