Consumer Protection of Unauthorized Cosmetic Distribution in Indonesia’s E-Commerce

Introduction to the Problem: Technological advances in the field of pharmacy or medicine, especially in the field of cosmetics today, have provided many alternatives for consumers, especially women, to meet their needs. The presence of various cosmetic products does provide hope for women to look more beautiful and attractive. But often, many cosmetics products have no active ingredients mentioned. Motivated by big profits, the manufacturers do not register their products for further assessment. When it comes to the market, the products are actually without standard authorization, or in the other cases using false or fictitious marketing authorization numbers. One form of protection to the consumer is the certainty of information contained in cosmetic packaging itself. The distribution of cosmetics without marketing authorization is also commonly found in applications or online stores. Still, on the one hand, it makes it easier for the public to conduct business transactions. However, compared with the easiness the online stores offered, when the products are still unauthorized, it will harm its users.Purpose/Objective Study: This research discusses consumer protection for the circulation of cosmetics without marketing authorization in Indonesia through e-commerce.Findings:This research shows that the Indonesian government protects consumers against the circulation of cosmetics without marketing authorization through the rules and laws. However, on the other aspect, those rules or regulations have no practical impact on society in this digital era. So, Indonesia should concern more to online activity where unauthorized marketing products are being marketed.

Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques

Data Science Technologies in Economics and Finance: A Gentle Walk-In

AbstractThis chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance

A holistic auto-configurable ensemble machine learning strategy for financial trading

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions

Technology Assessment for Cybersecurity Organizational Readiness: Case of Airlines Sector and Electronic Payment

Payment processing systems have advanced significantly in the airline business. Because e-payments are easy, they have captured the attention of many companies in the aviation industry and are quickly becoming the dominant means of payment. However, as technology advances, fraud grows at a comparable rate. Over the years, there has been a surge in payment fraud incidents in the airline sector, reducing the platform\u27s trustworthiness. Despite attempts to eliminate epayment fraud, decision-makers lack the technical expertise required to use the finest fraud detection and prevention assessments. This research recognizes the lack of an established decision model as a hurdle and seeks to fix the problem. In response, this research aims to develop a decision model for the airline industry to evaluate the e-payment fraud detection and prevention capabilities of airlines. The literature examines the scope of airline payment fraud to formulate the optimal framework to handle the problem. Guided by the results, the study proceeds to develop an HDM model from experts’ validation, quantification, and desirability inputs. The results of the factors’ validation and quantification show that the Economic and Financial, and the Security perspectives have the most impact on decision-making. Airline companies can use the developed framework to examine whether they are ready to adopt online fraud prevention technologies to increase their success rate. To measure payment organizations\u27 readiness for digital payment fraud protection technologies, a scoring methodology was developed in this research and applied to two case studies

Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model

More and more financial transactions through different E-commerce platforms have appeared now-days within the big data era bringing plenty of opportunities but also challenges and risks of stealing information for potential frauds that need to be faced. This is due to the massive use of tools such as credit cards for electronic payments which are targeted by attackers to steal sensitive information and perform fraudulent operations. Although intelligent fraud detection systems have been developed to face the problem, they still suffer from some well-known problems due to the imbalance of the used data. Therefore this paper proposes a novel data intelligence technique based on a Prudential Multiple Consensus model which combines the effectiveness of several state-of-the-art classification algorithms by adopting a twofold criterion, probabilistic and majority based. The goal is to maximize the effectiveness of the model in detecting fraudulent transactions regardless the presence of any data imbalance. Our model has been validated with a set of experiments on a large real-world dataset characterized by a high degree of data imbalance and results show how the proposed model outperforms several state-of-the-art solutions, both in terms of ensemble models and classification approaches

An Efficient Deep Learning Classification Model for Predicting Credit Card Fraud on Skewed Data

Due to fast-evolving technology, the world is moving to the use of credit cards rather than money in their daily lives, giving rise to many new opportunities for fraudsters to use credit cards maliciously. Based on the Nilson report, losses related to global cards were estimated to be over $35 billion by 2020. In order to maintain the security of users of these cards, the credit card company must develop a service to ensure that users are protected from any risks they may be exposed to. For this reason, we introduce a fraud detection model, denoted ST-BPNN, which is based on machine and deep learning approaches to identify fraudulent transactions. ST-BPNN was applied on real fraud detection data provided by the European bank. Comparing the obtained results from ST-BPNN with a recent state-of-the-art approach shows that our proposed model demonstrates high predictive performance for detecting fraudulent transactions

Sistema de Deteção de Transações Fraudulentas no e-commerce através de Machine Learning

O crescimento exponencial do comércio eletrónico trouxe inúmeras vantagens e oportunidades ao facilitar o estilo de vida dos seres humanos. No entanto, deu também origem a um grave problema: a fraude online. Com o propósito de colmatar este problema, este trabalho aborda a necessidade de desenvolver sistemas de deteção de fraude complexos no âmbito do comércio eletrónico. Após uma revisão abrangente da literatura, foram identificadas e implementadas técnicas que contribuíram para a melhoria dos projetos existentes, permitindo uma análise comparativa mais precisa. Neste contexto, os algoritmos de RF, LR, SVM, KNN, DT, LSTM e CNN, por serem os mais adequados a sistemas de classificação pela sua versatilidade e capacidade de aprender padrões complexos nos dados, foram aplicados a três conjuntos de dados distintos. Para avaliar rigorosamente os modelos propostos, o conjunto de dados foi dividido em 70% de dados para treino e os restantes 30% para teste. Cada um dos conjuntos de dados apresenta características específicas, de forma a avaliar o impacto de técnicas de oversampling e undersampling. Os algoritmos foram aplicados também aos mesmos conjuntos com os dados normalizados, para inferir quais os modelos que beneficiam desta normalização. Os resultados demonstraram que os modelos RF e CNN apresentaram um desempenho superior em comparação com os restantes algoritmos testados. Estes algoritmos foram posteriormente otimizados com a exploração dos hiper-parâmetros respetivos, o que permitiu melhorar o desempenho do modelo e, por sua vez, alcançar resultados de maior qualidade. A utilização de inteligência artificial na deteção de fraude no comércio eletrónico é fundamental para proteger os interesses tanto das empresas como dos consumidores. Este trabalho teve como foco principal contribuir para o avanço dos sistemas de deteção de transações fraudulentas ao fornecer informações sobre pontos positivos e negativos de vários algoritmos de machine learning no contexto do problema em questão.The exponential growth of e-commerce has brought numerous advantages and opportunities by facilitating the lifestyle of human beings. However, it has also given rise to a serious problem: online fraud. With the purpose of solving this problem, this work addresses the imperative need to develop complex fraud detection systems within the scope of electronic commerce. After a systematic review of the literature, different techniques were identified and implemented that contributed to the improvement of existing projects, allowing for a more accurate comparative analysis. In this context, the RF, LR, SVM, KNN, DT, LSTM and CNN algorithms, as they are the most suitable for classification systems due to their versatility and ability to learn complex patterns in data, were applied to three distinct datasets. To rigorously evaluate the proposed models, the dataset was divided into 70% training data and the remaining 30% to testing data. Each of the datasets consists in specific characteristics, in order to evaluate the impact of oversampling and undersampling techniques. The algorithms were also applied to the same datasets with normalized data, to infer which models benefit from this normalization. The results demonstrated that the RF and CNN algorithms presented superior performance compared to the remaining algorithms tested. These algorithms were subsequently optimized by exploring the respective hyper-parameters, which allowed improving the model's performance and, in turn, achieving higher quality results. The use of artificial intelligence to detect fraud in e-commerce is essential to protect the interests of both companies and consumers. This work's main focus was to contribute to the advancement of fraudulent purchase detection systems by providing information about the positive and negative points of various machine learning algorithms in the context of the problem in question