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...

Italian Abstract Il lavoro fornisce un quadro delle informazioni statistiche sul rischio di credito gestite dalla Banca d'Italia. L'Istituto ha una lunga tradizione nella raccolta sistematica e dettagliata di informazioni dal sistema finanziario. Con particolare riferimento a quelle sul rischio...

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...

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...

The paper illustrates the phases of the process that the Bank of Italy follows to produce the statistics derived from credit and financial reporting: the identification of the information requirements; the definition of the data model; the design of the new data collection method to be used by...

The paper presents a decision-making rule, based on statistical learning techniques, to evaluate and monitor the overall quality of the granular dataset referring to the Non-Performing Loans data collection carried out by the Bank of Italy. The datasets submitted by the reporting agents must...

The Bank of Italy collects a large amount of data — statistical, supervisory and resolution — from banks and other financial intermediaries in order to fulfil its institutional functions and meet the needs of other national and international authorities, in particular the ECB, the EBA and...

We exploit a novel dataset on mortgages offered by banks through Italy’s main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about...

Ensuring and disseminating high-quality data is crucial for central banks to adequately support monetary analysis and the related decision-making process. In this paper we develop a machine learning process for identifying errors in banks’ supervisory reports on loans to the private sector...