Credit card fraud detection in the era of disruptive technologies: A systematic review

Cherif, Asma; Badhib, Arwa; Ammar, Heyfa; Alshehri, Suhair; Kalkatawi, Manal; Imine, Abdessamad

doi:10.1016/j.jksuci.2022.11.008

Cited by 73 publications

(25 citation statements)

References 81 publications

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“…This article [9] reviews recent research on detecting credit card fraud from 2015 to 2021, categorizing 40 relevant articles based on topics and machine learning methods. It highlights the need for more exploration of deep learning and new technologies like big data analytics and cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Fraud Detection in Banking Using Cloud-Based Deep Learning Models

2024

IRJMETS

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Cloud computing's performance has been rapidly improving across a wide range of applications, including digital payments, storage, and access to sensitive data. Since many online apps available today use cloud-based electronic payment mechanisms, security and secrecy are essential. From 1990 to 2020, 42% of scams in various industries were discovered in India, according to the National Herald. These scams are increasing annually at large percentages. Fraudsters had no consistent patterns and always changed their behavior. So, implementation method becomes to be unsupervised learning mechanism. The ease of operation and low preservation has given an advantage of this clouds to use at high complex and critical accessible location for cloud monitoring. With the evolution of cloud based banking fraud detection system, various issues have arisen with its exploitation and maintenance. Hence, the design and implementation of cloud-based security system with the help of Deep Convolutional Neural Network (DCNN) based artificial bee Colony (ABC) are implemented. The cloud-based security system attains high precession it is the major requirement in cloud computing design. The fraudsters attack the cloud at traffic synchronized path with blocking control demand, using cloud link exchange. Therefore, this data exchange, communication systems are hacking through fault credentials. Here an adaptive machine learning scheme with deep learning arrangement is proposed. This approach operates on clouds, with central decision, privacy preserving mechanism, so fraud has been easily identified.

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Section: Related Workmentioning

confidence: 99%

Adaptive Fraud Detection in Banking Using Cloud-Based Deep Learning Models

2024

IRJMETS

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“…In addition to this process being easily understandable, implementing it is highly effective and easy. ML has a wide range of applications, including fraud detection in finance [ 42 ], personalized learning in education [ 43 ], disease diagnosis in healthcare [ 44 ], and climate modeling in environmental research [ 45 ]. ML helps solve challenges related to IoV, especially traffic flow prediction and optimization.…”

Section: Introductionmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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“…The utilization of AI increases risks that could impact a financial institution's stability due to the lack of clarity or interpretability of AI model operations, thereby leading to pro-cyclicality and systemic risk in the markets. The complexity of comprehending how the model produces outcomes could lead to potential conflicts with current financial oversight and internal management systems, and could also question the technology-agnostic approach to policy development [7,8]. AI has specific consumer protection risks, including biased, unfair, or discriminating outcomes for consumers, as well as concerns related to data management and utilization.…”

Section: Introductionmentioning

confidence: 99%

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

Pranav Kumar Chaudhary, Aakash Kishore Chotrani

2024

jes

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AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.

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Credit card fraud detection in the era of disruptive technologies: A systematic review

Cited by 73 publications

References 81 publications

Adaptive Fraud Detection in Banking Using Cloud-Based Deep Learning Models

Adaptive Fraud Detection in Banking Using Cloud-Based Deep Learning Models

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

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