Abstract
Algorithmic systems based on artificial intelligence (AI) increasingly play a role in decision-making processes, both in government and industry. These systems are used in areas such as retail, finances, and manufacturing. In the latter domain, the main priority is that the solutions are interpretable, as this characteristic correlates to the adoption rate of users (e.g., schedulers). However, more recently, these systems have been applied in areas of public interest, such as education, health, public administration, and criminal justice. The adoption of these systems in this domain, in particular the data-driven decision models, has raised questions about the risks associated with this technology, from which ethical problems may emerge. We analyse two important characteristics, interpretability and trustability, of AI-based systems in the industrial and public domains, respectively.

Abstract
The last few decades have witnessed a significant evolution of technology in different domains, changing the way the world operates, which leads to an overwhelming amount of data generated in an open-ended way as streams. Over the past years, we observed the development of several machine learning algorithms to process big data streams. However, the accuracy of these algorithms is very sensitive to their hyper-parameters, which requires expertise and extensive trials to tune. Another relevant aspect is the high-dimensionality of data, which can causes degradation to computational performance. To cope with these issues, this paper proposes a stream k-nearest neighbors (kNN) algorithm that applies an internal dimension reduction to the stream in order to reduce the resource usage and uses an automatic monitoring system that tunes dynamically the configuration of the kNN algorithm and the output dimension size with big data streams. Experiments over a wide range of datasets show that the predictive and computational performances of the kNN algorithm are improved.

Abstract
This paper uses non-traditional data, from a MicroFinance Institution (MFI), in a Credit Scoring loan classification problem and addresses a common problem in emerging markets of the lack of a verifiable customers' credit history. We perform a set of experiments to define a baseline model and prove the relevance of node embedding features, in credit scoring models, using a real world dataset. © 2020 IEEE.

Abstract

Abstract
The high asymmetry of international termination rates is fertile ground for the appearance of fraud in telecom companies. International calls have higher values when compared with national ones, which raises the attention of fraudsters. In this paper, we present a solution for a real problem called interconnect bypass fraud, more specifically, a newly identified distributed pattern that crosses different countries and keeps fraudsters from being tracked by almost all fraud detection techniques. This problem is one of the most expressive in the telecommunication domain, and it has some abnormal behaviours like the occurrence of a burst of calls from specific numbers. Based on this assumption, we propose the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm. We apply frequent set mining to capture distributed patterns from different countries. Our goal is to detect as soon as possible items with abnormal behaviours, e.g., bursts of calls, repetitions, mirrors, distributed behaviours and a small number of calls spread by a vast set of destination numbers. The results show that the application of different techniques improves the detection ratio and not only complements the techniques used by the telecom company but also improves the performance of the Lossy Counting algorithm in terms of run-time, memory used and sensibility to detect the abnormal behaviours. Additionally, the application of frequent set mining allows us to capture distributed fraud patterns.

Abstract
Fraud in telephony incurs huge revenue losses and causes a menace to both the service providers and legitimate users. This problem is growing alongside augmenting technologies. Yet, the works in this area are hindered by the availability of data and confidentiality of approaches. In this work, we deal with the problem of detecting different types of unsolicited users from spammers to fraudsters in a massive phone call network. Most of the malicious users in telecommunications have some of the characteristics in common. These characteristics can be defined by a set of features whose values are uncommon for normal users. We made use of graph-based metrics to detect profiles that are significantly far from the common user profiles in a real data log with millions of users. To achieve this, we looked for the high leverage points in the 99.99th percentile, which identified a substantial number of users as extreme anomalous points. Furthermore, clustering these points helped distinguish malicious users efficiently and minimized the problem space significantly. Convincingly, the learned profiles of these detected users coincided with fraudulent behaviors.