In conclusion, I would like to express the hope that the deliberations of the Forum, conducted on a confidential basis, will focus attention on the identification of potential systemic risks and on the promotion of effective risk management. I said earlier that complacency must be avoided. It has taken several years of hard work at both the political and technical levels to put Malta on the map as a reputable financial centre. A rigorous but non-intrusive regulatory regime based on international best practice combined with easy access to the single regulator has been a major plus. Other contributory factors are a legal framework based on common and civil law, 55 double-taxation treaties and a competitive fiscal regime, and, not least, membership of the euro area. Malta’s tradition of commercial banking based on a conservative funding model in which loans are entirely financed by retail deposits is a source of additional strength. Our banks, moreover, maintain capital and liquidity ratios well above the regulatory minimum and their resilience is confirmed by stress test results. We undoubtedly have reason to be satisfied. There can, however, be no letup. There is need for both a greater awareness about systemic risks and for enhanced efforts at risk mitigation and resilience building. We should not forget that there is a relatively high concentration of risk on both sides of bank balance sheets, in particular a significant exposure to the real estate sector, both in the form of loans and collateral held against them.
Chart 2 Exposure concentration and insurance as a mitigating factor  Sources: AnaCredit, 427 data, EMDAT, OECD and ECB calculations. Notes: Left panel: credit exposures to non-financial corporations (NFCs) above € are considered; € trillion of exposures overall; NFC location used to assign risk levels refers to the corporations’ headquarters; country breakdown refers to the bank’s country of residence. Right panel: sample includes 45 countries for which the OECD provides quarterly GDP data from 1996 to 2019. The chart shows the impact of large-scale natural disasters (i.e. with total damage larger than the third quartile at 0.1% of GDP) when the share of insured losses is high (above the median of 35%) on the left, and low (i.e. below the median of 35%) on the right. The estimates are obtained using a panel regression model where the dependent variable is the year-on-year difference in the log of GDP and the explanatory variables include two dummies capturing large-scale disasters with a high and low share of insured losses respectively (included with up to three lags), and country and quarterly fixed effects. For the quarter including the date(s) of the disaster (t=0) and the three subsequent quarters, the y-axis measures the percentage point impact of the disaster on the year-on-year annual growth rate at the end of that quarter. In principle, one mitigating factor may be insurance, which in the past has helped support aggregate demand and hastened reconstruction following disasters, reducing the overall impact on economic activity (Chart 2, right panel).