How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

Ryman-Tubb, Nick; Krause, Paul; Garn, Wolfgang

doi:10.1016/j.engappai.2018.07.008

Cited by 139 publications

(72 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Digital platforms in retail and banking have enabled customers to experience convenience through personalization and tailored technologies for shopping and performing transactions [1][2][3][4]. However, the convenience is also accompanied by the danger of frauds [5,6]. Transaction frauds are growing every year, and organizations such as retailers and banks realized the potential of AI models for automating the fraud detection task [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In finance, researchers seek to leverage explanations for better decision-making of fraud experts in reviewing fraudulent applications for credit and loans [13]. Therefore, a diversity of explanation methods for AI predictions has been developed in the XAI literature [6].…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The problem arises from the fact that the rate of actual fraud transactions out of all transactions is nominal. The number of legitimate transactions per day in 2017 completed by Tier-1 issuers is 5.7m, whereas fraud transactions in the same category is 1150 [17]. This unbalanced data distribution lessens the effectiveness of machine learning models [18].…”

Section: Imbalanced Dataset Problemmentioning

confidence: 99%

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

Yazıcı¹

2020

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.

show abstract

“…After the literature analysis, six dimensions related to digital connectivity in supply chains and logistics emerged, namely Smart, Innovative, Measurable, Profitable, Lean, and Excellence (SIMPLE). Fraud detection in payment card [14] Big data analytics Firm performance improvement [15] Physical Internet (PI)…”

Section: Proposing the Simple Frameworkmentioning

confidence: 99%

The Role of Digital Connectivity in Supply Chain and Logistics Systems: A Proposed SIMPLE Framework

Queiroz

Wamba

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Industry 4.0 and its related-cutting edge technologies are generating unprecedented changes and bringing complex challenges in practically all types of business. In this context, new concepts such as digital connectivity, interconnection, and interoperability emerged as highly disruptive approaches for logistics systems and supply chain management (SCM). To shed more light on these complexities and see how companies organized in a SCM model can adopt, implement and operate in a digital connectivity model, this study proposes a framework, namely SIMPLE. To develop the framework, we employed a literature review approach, focusing on recent studies published in journals. After the literature analysis, six dimensions related to digital connectivity in supply chains and logistics emerged, namely Smart, Innovative, Measurable, Profitable, Lean, and Excellence (SIMPLE). This framework brings opportunities for future studies, while providing important insights into the dynamics of digital connectivity in logistics and supply chains. Therefore, these SIMPLE framework dimensions should enable the actors involved in organizations' operations to interact adequately and harmoniously so as to maximize the value generated in the network.

show abstract

How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

Cited by 139 publications

References 61 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

The Role of Digital Connectivity in Supply Chain and Logistics Systems: A Proposed SIMPLE Framework

Contact Info

Product

Resources

About