Explainable Credit Card Fraud Detection with Image Conversion

Sinanç, Duygu; Demirezen, Umut; Sağıroğlu, Şeref

doi:10.14201/adcaij20211016376

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…2b). However, analysis of this joint population reveals just one paper published over the past 21 years which specifically investigates the application of XAI within a credit card fraud context [31].…”

Section: Scholarly Focusmentioning

confidence: 99%

Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Mill¹,

Garn²,

Ryman-Tubb³

et al. 2023

IJACSA

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Regulatory and technological changes have recently transformed the digital footprint of credit card transactions, providing at least ten times the amount of data available for fraud detection practices that were previously available for analysis. This newly enhanced dataset challenges the scalability of traditional rule-based fraud detection methods and creates an opportunity for wider adoption of artificial intelligence (AI) techniques. However, the opacity of AI models, combined with the high stakes involved in the finance industry, means practitioners have been slow to adapt. In response, this paper argues for more researchers to engage with investigations into the use of Explainable Artificial Intelligence (XAI) techniques for credit card fraud detection. Firstly, it sheds light on recent regulatory changes which are pivotal in driving the adoption of new machine learning (ML) techniques. Secondly, it examines the operating environment for credit card transactions, an understanding of which is crucial for the ability to operationalise solutions. Finally, it proposes a research agenda comprised of four key areas of investigation for XAI, arguing that further work would contribute towards a step-change in fraud detection practices.

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Section: Scholarly Focusmentioning

confidence: 99%

Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Mill¹,

Garn²,

Ryman-Tubb³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Iwana and Uchida [27] solved the TSC problem with two different types of inputs by considering the 1D CNN method. Sinanc et al [38] used a novel approach to convert the time series into images as the input and put these inputs in a CNN classifier. They used the gradient-weighted class activation mapping method to explain their CNN efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven operator functional state classification in smart manufacturing

Besharati Moghaddam,

Lopez,

Van Gheluwe

et al. 2023

Appl Intell

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One of the main challenges in the industry is having trained and efficient operators in manufacturing lines. Smart adaptive guidance systems are developed that offer assistance to the operator during assembly. Depending on the operator’s level of execution, the system should be able to serve a different guidance response. This paper investigates the assessment and classification of the operator’s functional state using observed task execution times. Five different classifiers are studied for operator functional state classification on task execution time series. The experiments are based on an industry case and the ground truth is provided by an expert rule-based system. Three classification scenarios are defined that segment the problem on the level of the task, the individual, or the team. Furthermore, the investigation includes the evaluation of four distinct window-size configurations. The examination of how these scenarios and window-sizes influence the studied dataset across diverse classifiers reveals that achieving enhanced accuracy necessitates a larger input dimension. In this context, Convolutional Neural Networks predominantly exhibit superior performance compared to alternative classifiers. Careful attention needs to be paid to performance over classes and skills, but results confirm the validity of the approach for data-driven operator functional state classification.

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“…However, increases in the volume and velocity of credit card transactions can cause class imbalance and concept deviation problems in datasets where credit card fraud is detected, which may make it very difficult for traditional approaches to produce robust detection models. To address this, Sinanc et al [110] proposed a novel approach called fraud detection with image conversion.…”

Section: Promising Research Directionsmentioning

confidence: 99%

Emerging Trends in Deep Learning for Credit Scoring: A Review

Hayashi

2022

Electronics

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This systematic review aims to provide deep insights on emerging trends in, and the potential of, advanced deep learning techniques, such as machine learning algorithms being partially replaced by deep learning (DL) algorithms for credit scoring owing to the higher accuracy of the latter. This review also seeks to explain the reasons that deep belief networks (DBNs) can achieve higher accuracy than shallower networks, discusses the potential classification capabilities of DL-based classifiers, and bridges DL and explainable credit scoring. The theoretical characteristics of DBNs are also presented along with the reasons for their higher accuracy compared to that of shallower networks. Studies published between 2019 and 2022 were analysed to review and compare the most recent DL techniques that have been found to achieve higher accuracies than ensemble classifiers, their hybrids, rule extraction methods, and rule-based classifiers. The models reviewed in this study were evaluated and compared according to their accuracy and area under the receiver operating characteristic curve for the Australian, German (categorical), German (numerical), Japanese, and Taiwanese datasets, which are commonly used in the credit scoring community. This review paper also explains how tabular datasets are converted into images for the application of a two-dimensional convolutional neural network (CNN) and how “black box” models using local and global rule extraction and rule-based methods are applied in credit scoring. Finally, a new insight on the design of DL-based classifiers for credit scoring datasets is provided, along with a discussion on promising future research directions.

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Explainable Credit Card Fraud Detection with Image Conversion

Cited by 8 publications

References 26 publications

Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Data-driven operator functional state classification in smart manufacturing

Emerging Trends in Deep Learning for Credit Scoring: A Review

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