Управление рисками при проведении операций с использованием платежных банковских карт
Real time monitoring of all transactions that uses deposit and credit plastic cards is necessary condition to decrease probability of illegal activity. Information systems of fraud detection are usually expensive and calls for power hardware platform. Unfortunately they can’t exclude fraud at all. Results of this article give the ability to decrease total cost of ownership for such systems. They are founded on the research of the activity of one russian bank.
This article focuses on the legal aspects of information technologies used in the national system of payment cards called Mir (the World). The creation and the software design independent of the information system for processing transactions conducted using Bank plastic cards Mir (the World), demanded the implementation of their own development and independent technological solutions, particularly in the area of information security. For realization of tasks in order to ensure the independence of the created Russian national payment system Mir (the World) from the international payment systems and providers of information technology solutions was formed a special regulatory framework, which became the subject of the present study. For a long time the lack of a single specialized operational and payment clearing center of the national level, providing services for funds transfers using payment cards and other electronic payment instruments, as well as the dominant role of the international payment systems on the Russian space for the provision of payment services, has hampered the development of the national payment system. In the course of the study, including local legal rules established for implementation of its own national payment system, revealed significant results of work by Russian developers, aimed at the promotion and development of national payment instruments to achieve a significant share in the total volume of the market of both domestic and cross-border transfers of funds using national electronic funds payment card Mir (the World) and the prospects of structural changes of the Russian market of retail payment services, leading to less dependence on international payment systems. Источник: http://itportal.ru/science/tech/natsionalnaya-platezhnaya-sistema-m/
Intelligent computer systems aim to help humans in making decisions. Many practical decision-making problems are classification problems in their nature, but standard classification algorithms often not applicable since they assume balanced distribution of classes and constant misclassification costs. From this point of view, algorithms that consider the cost of decisions are essential since they are more consistent with the requirements of real life. These algorithms generate decisions that directly optimize parameters valuable for business, for example, the costs savings. But despite on practical value of cost-sensitive algorithms, the little number of works study this problem concentrating mainly on the case when the cost of a classifier error is constant and does not depend on a specific example. However, many real-world classification tasks are example-dependent cost-sensitive (ECS), where the costs of misclassification vary between examples and not only within classes. Existing methods of ECS learning include just modifications of the simplest models of machine learning (naive Bayes, logistic regression, decision tree). These models produce promising results, but there is a need for further improvement in performance that can be achieved by using gradient-based ensemble methods. To break this gap, we present the ECS generalization of AdaBoost. We study three models which differ by the ways to introduce cost into the loss function: inside the exponent, outside the exponent, and both inside and outside the exponent. The results of the experiments on three synthetic and two real datasets (bank marketing and insurance fraud) show that example-dependent cost-sensitive modifications of AdaBoost outperform other known models. Empirical results also show that critical factors influencing the choice of the model are not only the distribution of features, which is typical for cost-insensitive and class-dependent cost-sensitive problems but also the distribution of costs. Next, since the outputs of AdaBoost are not well calibrated posterior probabilities, we check three approaches to calibration of classifier scores: Platt scaling, isotonic regression, and ROC modification. The results show that calibration not only significantly improves the performance of specific ECS models but allows making better capabilities of original AdaBoost. Obtained results provide new insight regarding the behavior of the cost-sensitive model from a theoretical point of view and prove that the presented approach can significantly improve the practical design of intelligent systems.