The role of IT/IS in combating fraud in the payment card industry

The vast growth of the payment card industry (PCI) in the last 50 years has placed the industry in the centre of attention, not only because of this growth, but also because of the increase of fraudulent transactions. The conducted research in this domain has produced statistical reports on detection of fraud, and ways of protection. On the other hand, the relevant body of research is quite partial and covers only specific topics. For instance, the provided reports related to losses due to fraudulent usage of cards usually do not present the measures taken to combat fraud nor do they explain the way fraud happens. This can turn out to be confusing and makes one believe that card usage can be more negative than positive. This paper is intended to provide accumulative and organized information of the efforts made to protect businesses from fraud. We try to reveal the effectiveness and efficiency of the current fraud combating techniques and show that organized worldwide efforts are needed to take care of the larger part of the problem. The research questions that will be addressed in the paper are: 1) how can IT/IS help in combating fraud in the PCI?, and 2) is the implemented IT/IS effective and efficient enough to bring progress in combating fraud? Our research methodology is based on a case study conducted in a Macedonian bank. The research is explorative and will be mostly qualitative in nature; however some quantitative aspects will be included. The findings indicate that fraud can take up many forms. A classification of the different forms of data theft into different fraudulent appearances was made. We showed that the benefits from implementing the fraud reduction efforts are multiple. Results show that a bank has to be very small to experience losses from fixed expenditures coming from the implementation of the fraud reduction IT/IS. Medium-sized and large banks should not even see any problems arising from those expenditures. Based on the empirical data and the presented facts we can conclude that the fraud reduction IT/IS do have a positive effect on all sides of the payment process and fulfills the expectations of all stakeholders

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Huddl: the Hydrographic Universal Data Description Language

Since many of the attempts to introduce a universal hydrographic data format have failed or have been only partially successful, a different approach is proposed. Our solution is the Hydrographic Universal Data Description Language (HUDDL), a descriptive XML-based language that permits the creation of a standardized description of (past, present, and future) data formats, and allows for applications like HUDDLER, a compiler that automatically creates drivers for data access and manipulation. HUDDL also represents a powerful solution for archiving data along with their structural description, as well as for cataloguing existing format specifications and their version control. HUDDL is intended to be an open, community-led initiative to simplify the issues involved in hydrographic data access

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed