Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services

This study aims to determine the best practices and provide a model of the technical solutions that can effectively and systematically limit fraudulent transactions of online orders in e-commerce services, using the methods of analytical mining and case studies. Based on a process of fraud prevention and detection performed in the e-business Dangdang, Inc., a leading online retailer in China, twelve identifying features of fraudulent order data were extracted and compiled into a feature matrix. Logistic regression with this matrix was then used to build a model to judge if an order was fraudulent. The model was tested using various order data with machine learning techniques to meet the requirements of being effective, correct, adaptive, and persistent. Then an online detection and prevention schema was established and the hypothesis of so-called Behavior Pattern Change Assumption (BPCA) was proven. The results show the model can detect 94% of fraudulent orders. The Anti-fraud Schema System established for Dangdang is shown to be the best model for the determination and prevention of fraudulent behaviors in the e-commerce services

A framework for detecting financial statement fraud through multiple data sources

This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial infor-mation, they are the focus. This project focuses on the top five market sectors where fraud is most common. It focuses on a variety of fraud types, but not on cases of deception that do not constitute fraud. A framework will be proposed which ac-counts for both structured data (the numbers in the balance sheet, income statement and cash flow statement) and unstruc-tured data (the footnotes in these financial statements). It uses ontology-driven data mining techniques to do so

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area

Machine Learning Techniques for Credit Card Fraud Detection

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher

A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection

Now a day’s credit card transactions have been gaining popularity with the growth of e-commerce and shows tremendous opportunity for the future. Therefore, due to surge of credit card transaction, it is a crying need to secure it . Though the vendors and credit card providing authorities are showing dedication to secure the details of these transactions, researchers are searching new scopes or techniques to ensure absolute security which is the demand of time. To detect credit card fraud, along with other technologies, applications of machine learning and computational intelligence can be used and plays a vital role. For detecting credit card anomaly, this paper analyzes and compares some popular classifier algorithms. Moreover, this paper focuses on the performance of the classifiers. UCSD -FICO Data Mining Contest 2009 dataset were used to measure the performance of the classifiers. The final results of the experiment suggest that (1) meta and tree classifiers perform better than other types of classifiers, (2) though classification accuracy rate is high but fraud detection success rate is low. Finally, fraud detection rate  should be taken into consideration to assess the performance of the classifiers in a credit card fraud detection system

Systemic acquired critique of credit card deception exposure through machine learning

Artigo publicado em revista científica internacionalA wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.info:eu-repo/semantics/publishedVersio

xFraud: Explainable Fraud Transaction Detection

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15 (3) (VLDB 2022

A New Prototype for Intelligent Visual Fraud Detection in Agent-Based Auditing Framework

While US. Sarbanes Oxley act has been viewed by most as an onerous and expensive requirement; it is having a positive impact on driving appropriate levels of investment in IT security, controls, and transactional systems. This paper introduces a new secure solution for auditing and accounting based on artificial intelligence technology. These days, security is a big issue among regulatory firms. Big companies are concerned about their data to be disseminated to their competitors; this high risk prevents them to provide full information to the regulatory firms. This solution not only significantly reduces the risk of unauthorized access to the company’s information but also facilitate a framework for controlling the flow of disseminating information in a risk free method. Managing security is performed by a network of mobile agents in a pyramid structure among regulatory organization like securities and exchanges commissions, stock exchanges in top of this pyramid to the companies in the button. Because of security considerations, our strategy is to delegate all fraud detection algorithms to Intelligent Mobile Auditing Agent and web service undertake all inter communicational activity. Web services can follow auditing actives in predefined framework and they can act based on permitted security allowance to auditors. The current solution is designed based on Java-based mobile agents. Such design reaps strong mobility and security benefits. This new prototyped solution could be a framework for strengthening security for future development in this area. An insider trading case study is used to demonstrate and evaluate the approach

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic