##ries and investors may well affect the stability of the system. public authorities must carefully examine the changes induced by digital technologies and value innovative projects to preserve the stability, the efficiency, and the security of the financial sector. the complexity is increased by the fact that central banks are at once both users and producers of information and big data. proof of this is the collection of highly granular data on individual bank loans by the single supervisory mechanism ( the so called anacredit ), on daily money market statistical reporting by the european system of central banks, and on the trading repositories envisaged by the european market infrastructure regulation. the sheer amount of these granular data makes it clear that the necessary validation processes cannot be performed manually, but will require sophisticated algorithms and techniques such as machine learning or artificial intelligence. central banks should master these new technologies. the bank of italy has created an internal multidisciplinary team on big data which includes economists, statisticians and computer scientists from different departments, working in close cooperation with the directorate general for information technology. the team has built a hardware and software infrastructure in order to deal with different kinds of big data for both macroeconomic and microeconomic issues. some of the results will be discussed during this workshop. the analyses focus on using unstructured textual data from social media, in particular twitter, to compute inflation expectations4 or to gauge retail depositors ’ trust. 5 see c. angelico, j. marcucci, m. miccoli, and f. quarta, ‘ can we measure inflation expectations using twitter? ’. m. accornero and m. moscatelli in ‘ listening to the buzz : social media sentiment and retail depositors ’ trust ’, a measure of retail depositors ’ trust by checking twitter comments. banca d ’ italia, temi di discussione ( working papers ), 1165 ( 2018 ). social media are also used to assess customers ’ sentiment towards specific companies and its effect on stock returns, volatility and trading volumes. 6 twitter and news are used to measure economic policy uncertainty and to focus on payment card scams, to then relate them to trends in consumer payments. another strand of research uses data from single online real estate ads extracted from the web to understand the microstructure of the italian real estate market. 7 let me conclude by thanking once again all the speakers, discussants, panelists and participants for being here. i look forward to

of euros ) source : based on dealogic. figure 5. bond yields ( daily data ; amounts and percentage points source : based on bloomberg. figure 6. relationship between the npl ratio and time for enforcing contacts ( eu area ; percentage points and number of days ) cyprus greece ∆ npl ratio ( % ) ireland y = 0, 023x - 4, 7845 r² = 0, 4175 italy slovenia - 5 - 1. 000 1. 200 1. 400 1. 600 time for resolving a dispute through a local first - instance court ( # of days ) source : ecb consolidated banking data and world bank doing business database. 1. 800 figure 7. cost of risk and roe ( annualized data ; basis points and percentage points ) source : bank of italy. figure 8. gross income : dynamic of interest and non - interest income ( annualized data ; indices, 100 = december 2008 ) source : bank of italy. figure 9. sources of funding and leverage of italian firms ( annual flows in billions of euro and per cent ) source : bank of italy and cerved. ( 1 ) leverage is calculated as the ratio of financial debt to the sum of financial debt and net equity at market prices. ( 2 ) adjusted leverage is calculated by removing the effects of changes in the market value of net equity. a value above ( below ) the solid line indicates, for a given year, a decrease ( increase ) in the market value of equity. designed by the printing and publishing division of the bank of italy