This paper provides an overview of how information on payments has been recently exploited by Banca d'Italia staff for the purposes of tracking economic activity and forecasting. In particular, the payment data used for this work are drawn from the payment systems managed by Banca d'Italia (BI-COMP and TARGET2) and from the Anti-Money Laundering Aggregate Reports submitted by banks and by Poste Italiane to the Banca d'Italia's Financial Intelligence Unit (Unità di Informazione Finanziaria, UIF). We show that indicators drawn from these sources can improve forecasting accuracy; in particular, those available at a higher frequency have proved crucial to properly assessing the state of the economy during the pandemic. Moreover, these indicators make it possible to assess changes in agents' behaviour, notably with reference to payment habits, and, thanks to their granularity, to delve deeper into the macroeconomic trends, exploring heterogeneity by sector and geography.

We exploit a gravity model to study the main determinants of cross-border financial flows and to identify those flows that appear to be abnormally above the predicted value. Our data include all Italian cross-border bank transfers that took place between 2007 and 2010. We find that, other things being equal, financial flows to risky destinations are 36 per cent larger than in other countries. Using the residuals from our main econometric specification, we then construct an index of anomaly and find positive and statistically significant correlations between this and the rate of property and drugs-related crimes in the province of origin, and also between the index and other measures of foreign jurisdictions' riskiness and opacity of legislation.