A Hybrid Deep Learning Model For Online Fraud Detection

Xiong, Kewei; Peng, Binhui; Jiang, Yang; Lu, Tiying

doi:10.1109/iccece51280.2021.9342110

Cited by 20 publications

(8 citation statements)

References 7 publications

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“…Furthermore, Kewei et al [159] proposed a deep learningbased ensemble for detecting online fraud. The method combined the binary cross-entropy (BCE) loss and Focal loss to obtain the proposed model's loss function.…”

Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…Na Tabela 2, é possível observar os resultados apresentados pelo melhor modelo encontrado pela abordagem proposta para cada um dos cinco algoritmos de classificac ¸ão investigados (Logistic Regression, Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF) e XGBoost, bem como os resultados reportados pelos principais trabalhos relacionados: [Deng et al 2021], [Kewei et al 2021], [Nguyen et al 2022], [Chen e Han 2021], [Ni et al 2023] e [Bakhtiari, Nasiri e Vahidi 2023]. Vale destacar que todos esses trabalhos utilizaram o conjunto de dados IEE-CIS Fraud Detection.…”

Section: Discussão Dos Resultadosunclassified

“…No estudo [Kewei et al 2021], técnicas de aprendizado profundo e engenharia de atributos foram combinadas para detectar fraudes em transac ¸ões online. A adic ¸ão de novos atributos com base em medidas estatísticas e informac ¸ões temporais resultou em um modelo mais eficiente, reduzindo a dimensionalidade do conjunto de dados original de 435 atributos.…”

Section: Trabalhos Relacionadosunclassified

Arquitetura de Tempo Real e Modelo de Aprendizado de Máquina para Detecção de Fraudes de Cartão de Crédito

Santos,

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Rego

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2023)

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Diante da necessidade crescente de sistemas seguros e eficientes para detectar fraudes em transações de cartões de crédito em tempo real, este estudo propõe e avalia uma abordagem de detecção de fraude baseada em aprendizado de máquina, integrada a uma arquitetura em tempo real de um banco virtual. A arquitetura multicamadas utilizada permite uma clara divisão de responsabilidades, resultando em operações otimizadas e eficientes. Com o uso do modelo de aprendizado de máquina Random Forest, o sistema é capaz de fazer previsões precisas sobre possíveis transações fraudulentas. Essa implementação fornece resultados robustos e confiáveis, com acurácia de 99,98%, precisão de 99,97%, revocação de 100% e F1-score de 99,88%, destacando o potencial do aprendizado de máquina para melhorar significativamente a eficácia da detecção de fraudes.

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“…The model is trained and evaluated on the IEEE-CIS fraud dataset. Their experiments show that the model outperforms traditional machine-learning-based methods like Bayes and SVM on the used dataset [18].…”

Section: Related Workmentioning

confidence: 99%

Fraud Detection in Banking Data by Machine Learning Techniques

2023

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As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weighttuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

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A Hybrid Deep Learning Model For Online Fraud Detection

Cited by 20 publications

References 7 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Arquitetura de Tempo Real e Modelo de Aprendizado de Máquina para Detecção de Fraudes de Cartão de Crédito

Fraud Detection in Banking Data by Machine Learning Techniques

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