Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

The need to focus on banks' funding structure and stress testing in an explicit way arose as a consequence of the crisis of past decades. Liquidity risks usually occur as a consequence of other kinds of risks, hence analysing scenarios in a prospective manner is essential for the assessment if...

In the context of the data quality management of supervisory banking data, the Bank of Italy receives a significant number of data reports at various intervals from Italian banks. If any anomalies are found, a quality remark is sent back, questioning the data submitted. This process can lead to...

This paper addresses the issue of assessing the quality of granular datasets reported by banks via machine learning models. In particular, it investigates how supervised and unsupervised learning algorithms can exploit patterns that can be recognized in other data sources dealing with similar...

We propose a new methodology, based on machine learning algorithms, for the automatic detection of outliers in the data that banks report to the Bank of Italy. Our analysis focuses on granular data gathered within the statistical data collection on payment services, in which the lack of strong...

Serbian: У раду се анализирају нови приступи оцењивању степена концентрације и конкуренције. Као пример одабран је сектор осигурања у Србији, а за променљиву на...

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

We implement machine learning techniques to obtain an automatic classification by sector of economic activity of the Italian companies recorded in the Bank of Italy Entities Register. To this end, first we extract a sample of correctly classified corporations from the universe of Italian...