Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection

The rapid expansion of e-commerce has significantly transformed consumer purchasing behaviors, but it has also given rise to the sale of black market goods through online platforms, raising critical concerns about consumer protection and regulatory oversight. This study investigates the responsibilities of e-commerce platforms regarding the sale of black market products and evaluates the effectiveness of legal and regulatory frameworks currently in place. Adopting a normative juridical research method, this study involves a thorough analysis of existing laws, regulations, and industry standards pertaining to e-commerce and black market transactions. By examining legal texts, regulatory guidelines, and case law, this research identifies gaps in the current legal framework and assesses its adequacy in addressing black market activities. The results indicate that while legal provisions exist, they are often insufficiently enforced and lack clarity in their application to emerging e-commerce practices. The study concludes that e-commerce platforms must advocate for stronger legal reforms and clearer regulations to enhance consumer protection. Additionally, improved transparency in platform policies and heightened regulatory scrutiny are necessary to better address the challenges posed by black market goods. Future research should explore the development of more comprehensive legal strategies and frameworks to effectively combat black market transactions in the digital realm. 

E-Commerce Liability to Consumers For the Sale of Black Market Products

The Effect of PCI DSS Implementation on Customer Retention and Fraud Prevention in E-Commerce Platforms from E-store Owners’ Viewpoints

Abstract Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms. 

KDGAN: Knowledge distillation‐based model copyright protection for secure and communication‐efficient model publishing

The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry. 

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises' overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58. 

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Spurred on by the ‘reproducibility crisis’, social scientists are starting to adopt research transparency practices. Research funders are largely unaware that replication work could strengthen the ...

Demand driven replication research: an overview of financial services for the poor replication research

Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.

The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach

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E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Demand driven replication research: an overview of financial services for the poor replication research

The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach