allowed them to instill a quantitative approach to their off-site monitoring, with techniques that can summarize large amounts of data into a few quantitative assessments. Although the reliance on databases is not a perfect substitution for onsite supervision, the process for off-site monitoring has particularly been facilitated by those jurisdictions that utilize the PCR to produce more reliable and useful estimations of bank risk; for example, those that use PCRs in techniques, such as risk assessment and early warning systems, credit portfolio models and stress tests, and need detailed data on banks credits.

- **Supervisory risk assessment and early warning systems** . Based to a large extent on the experience with credit scoring models and rating systems by the banking industry, supervisors developed their own systems to quantify and rank banks’ performance in general, and to measure their loan portfolio quality in particular. In a review of the supervisory approaches for bank monitoring in G10 countries, Sahajwala and Van den Bergh (2000) grouped the diverse systems into four categories: supervisory bank rating systems, financial ratio and peer group analysis systems, comprehensive bank risk assessment systems and statistical models.<sup>23</sup> To the extent that credit risk is the major driver of bank losses, the ability to count with detailed PCR data enhances the quality of these systems and the reliability of their assessments. For example, the off-site rating system used at the Bank of Italy (the PATROL rating system)<sup>24</sup> measures credit quality and loan concentration with PCR data.

- **Bank portfolio credit risk** . When monitoring banks and assessing their capacity to withstand losses for credit risk, the adequacy of their provisions and capital levels can be evaluated by means of transition matrices and portfolio credit risk models (CreditRisk+<sup>25</sup> , Internal Ratings Based approach - IRB -, etc.) that incorporate PCR data. Transition matrices are useful devices to analyze the dynamics of portfolio credit risk - they show how borrowers risk grading has evolved in the past, what their average default rate has been and how far it has deviated from that average (its volatility). Portfolio models also exploit historical credit data, but are more efficient in that they better depict loan portfolio loss distributions. Either way, these techniques can provide insight as to banks’ ability to withstand expected and unexpected losses. They are of particular importance in those jurisdictions that have yet to adopt risk based capital and provisioning requirements for credit risk.

- **Stress tests** . Although banks (in particular, large and/or internationally active ones) perform stress tests as part of their credit risk management policies, central banks and bank supervisors are increasingly performing their own stress testing exercises.<sup>26</sup> These supervisory stress tests are designed to gauge the

> 23 These comprise models for ratings or rating downgrades, survival prediction and expected loss models, and others for obtaining early warning signals.

> 24 Marullo Reedtz and Trapanese (2000) find empirical evidence for the importance of the information in the PCR of the Bank of Italy in building off-site early warning techniques of bank frailty. 25 CreditRisk+ is a credit portfolio model developed in Credit Suisse First Boston (1997).

> 26 When reviewing banks stress tests, supervisors should at the minimum be wary of their results: “ _A few years ago, ahead of the present crisis, the Bank of England and the FSA commenced a series of seminars with financial firms, exploring their stress-testing practices. The first meeting of that group sticks in my mind. We had asked firms to tell us the sorts of stress which they routinely used for their stress-tests. A quick survey suggested these were very modest stresses. We asked why. Perhaps_

9