The rating of users requesting financial services is a growing task, especially in this historical period of the COVID-19 pandemic characterized by a dramatic increase in online activities, mainly related to e-commerce. This kind of assessment is a task manually performed in the past that today needs to be carried out by automatic credit scoring systems, due to the enormous number of requests to process. It follows that such systems play a crucial role for financial operators, as their effectiveness is directly related to gains and losses of money. Despite the huge investments in terms of financial and human resources devoted to the development of such systems, the state-of-the-art solutions are transversally affected by some well-known problems that make the development of credit scoring systems a challenging task, mainly related to the unbalance and heterogeneity of the involved data, problems to which it adds the scarcity of public datasets. The Region-based Training Data Segmentation (RTDS) strategy proposed in this work revolves around a divide-and-conquer approach, where the user classification depends on the results of several sub-classifications. In more detail, the training data is divided into regions that bound different users and features, which are used to train several classification models that will lead toward the final classification through a majority voting rule. Such a strategy relies on the consideration that the independent analysis of different users and features can lead to a more accurate classification than that offered by a single evaluation model trained on the entire dataset. The validation process carried out using three public real-world datasets with a different number of features. samples, and degree of data imbalance demonstrates the effectiveness of the proposed strategy. which outperforms the canonical training one in the context of all the datasets.

The relatively recent introduction on the market of low-cost devices able to perform an Electroencephalography (EEG) has opened a stimulating research scenario that involves a large number of researchers previously excluded due to the high costs of such hardware. In this regard, one of the most stimulating research fields is focused on the use of such devices in the context of biometric systems, where the EEG data are exploited for user identification purposes. Based on the current literature, which reports that many of these systems are designed by combining the EEG data with a series of external stimuli (Evoked Potentials) to improve the reliability and stability over time of the EEG patterns, this work is aimed to formalize a biometric identification system based on low-cost EEG devices and simple stimulation instruments, such as images and sounds generated by a computer. In other words, our objective is to design a low-cost EEG-based biometric approach exploitable on a large number of real-world scenarios.

The problem of frauds is becoming increasingly important in this E-commerce age, where an enormous number of financial transactions are carried out by using electronic instruments of payment such as credit cards. In this scenario it is not possible to adopt human-driven solutions due to the huge number of involved operations. The only approach is therefore to adopt automatic solutions able to discern the legitimate transactions from the fraudulent ones. For this reason, today the development of techniques capable of carrying out this task efficiently represents a very active research field that involves a large number of researchers around the world. Unfortunately, this is not an easy task, since the definition of effective fraud detection approaches is made difficult by a series of well-known problems, the most important of them being the non-balanced class distribution of data that leads towards a significant reduction of the machine learning approaches performance. Such limitation is addressed by the approach proposed in this paper, which exploits three different metrics of similarity in order to define a three-dimensional space of evaluation. Its main objective is a better characterization of the financial transactions in terms of the two possible target classes (legitimate or fraudulent), facing the information asymmetry that gives rise to the problem previously exposed. A series of experiments conducted by using real-world data with different size and imbalance level, demonstrate the effectiveness of the proposed approach with regard to the state-of-the-art solutions.

Nowadays, the prevention of credit card fraud represents a crucial task, since almost all the operators in the E-commerce environment accept payments made through credit cards, aware of that some of them could be fraudulent. The development of approaches able to face effectively this problem represents a hard challenge due to several problems. The most important among them are the heterogeneity and the imbalanced class distribution of data, problems that lead toward a reduction of the effectiveness of the most used techniques, making it difficult to define effective models able to evaluate the new transactions. This paper proposes a new strategy able to face the aforementioned problems based on a model defined by using the Discrete Fourier Transform conversion in order to exploit frequency patterns, instead of the canonical ones, in the evaluation process. Such approach presents some advantages, since it allows us to face the imbalanced class distribution and the cold-start issues by involving only the past legitimate transactions, reducing the data heterogeneity problem thanks to the frequency-domain-based data representation, which results less influenced by the data variation. A practical implementation of the proposed approach is given by presenting an algorithm able to classify a new transaction as reliable or unreliable on the basis of the aforementioned strategy.

The massive increase in financial transactions made in the e-commerce field has led to an equally massive increase in the risks related to fraudulent activities. It is a problem directly correlated with the use of credit cards, considering that almost all the operators that offer goods or services in the e-commerce space allow their customers to use them for making payments. The main disadvantage of these powerful methods of payment concerns the fact that they can be used not only by the legitimate users (cardholders) but also by fraudsters. Literature reports a considerable number of techniques designed to face this problem, although their effectiveness is jeopardized by a series of common problems, such as the imbalanced distribution and the heterogeneity of the involved data. The approach presented in this paper takes advantage of a novel evaluation criterion based on the analysis, in the frequency domain, of the spectral pattern of the data. Such strategy allows us to obtain a more stable model for representing information, with respect to the canonical ones, reducing both the problems of imbalance and heterogeneity of data. Experiments show that the performance of the proposed approach is comparable to that of its state-of-the-art competitor, although the model definition does not use any fraudulent previous case, adopting a proactive strategy able to contrast the cold-start issue.