Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning

Habibpour, Maryam; Gharoun, Hassan; Mehdipour, Mohammadreza; Tajally, AmirReza; Asgharnezhad, Hamzeh; Shamsi, Afshar; Khosravi, Abbas; Shafie‐khah, Miadreza; Nahavandi, Saeid; Catalão, João P.S.

doi:10.48550/arxiv.2107.13508

Cited by 3 publications

(3 citation statements)

References 29 publications

(39 reference statements)

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“…Credit card fraud detection using uncertainty-aware DL was implemented in Habibpour et al (2021) . It is vital to evaluate the uncertainty of DNN predictions.…”

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.

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“…Credit card fraud detection using uncertainty-aware DL was implemented in Habibpour et al (2021) . It is vital to evaluate the uncertainty of DNN predictions.…”

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

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“…The predictive uncertainty of DNNs can be divided into two main subtypes: epistemic and aleatoric or data uncertainty [36], [37], [38]. Epistemic uncertainty can be formalized by means of a probability distribution over the model parameters and accounts for our unawareness about them.…”

Section: Studymentioning

confidence: 99%

An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Shamsi¹,

Asgharnezhad²,

Tajally³

et al. 2021

Preprint

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Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result. In recent years, a numerous number of uncertainty quantification methods have been introduced. Monte Carlo dropout (MC-Dropout) is one of the most wellknown techniques to quantify uncertainty in deep learning methods. In this study, we propose two new loss functions by combining cross entropy with Expected Calibration Error (ECE) and Predictive Entropy (PE). The obtained results clearly show that the new proposed loss functions lead to having a calibrated MC-Dropout method. Our results confirmed the great impact of the new hybrid loss functions for minimising the overlap between the distributions of uncertainty estimates for correct and incorrect predictions without sacrificing the model's overall performance.

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“…I NFORMATION technology advancements have significantly impacted the financial sector, leading to the broad adoption of electronic commerce (e-commerce) platforms. Also, the recent outbreak of the novel coronavirus (COVID-19) pandemic has further shown the need for a more digital world and further expanded the e-commerce industry [1], [2]. One of the major issues associated with modern e-commerce is the high cases of credit card fraud [3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

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Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning

Cited by 3 publications

References 29 publications

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

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