jurisdictions they must also rate their borrowers with a mandatory system prescribed by the regulator and based only on a few criteria, such as type of credit, days past due and projected cash flow.<sup>14</sup> These “prudential ratings” have typically been introduced as a means to homogenize the criteria to compute regulatory loan loss provisions and to assess the credit quality of different bank portfolios with the same metric, particularly in jurisdictions where banks’ rating systems were yet not widespread.<sup>15</sup> Although these “prudential rating systems” are not aimed at replacing or competing with banks’ rating systems,<sup>16</sup> they can play a subsidiary role. For example, smaller banks may be limited in their capacity to develop their own rating systems because of insufficient data, experience or resources; in such cases, the prudential ratings could supplement better risk analysis. Well constructed regulatory/supervisory rating systems may contribute to leveling the playing field in bank risk management practices and at the same time it would familiarize less advanced banks with modern credit risk management tools.<sup>17</sup> Statistical evidence for Argentina in Majnoni _et al_ (2004a) shows that a rating system built with PCR data benefits smaller banks with a larger reduction in their loan portfolio credit risk, as compared to that observed in bigger banks.

In jurisdictions where mandatory rating systems are in place, transition matrices coupled with assumptions regarding recovery rates can indicate to what extent provisioning ratios are aligned with average risk. Computing credit portfolio models with PCR data can also assist in optimizing the design and calibration of the regulatory framework, since they typically yield loss distributions that depict banks’ risk profile.<sup>18</sup> The output of credit portfolio models can be used to verify that credit loss provisions, obtained from applying the supervisory ratios, globally match the estimated expected loss and that regulatory capital covers most unexpected losses. Academic attempts to perform such comparisons can be found in Balzarotti _et al_ (2002) - with Central Bank of Argentina PCR data -, in Cronemberger Parente _et al_ (2004) - with data from the Central Bank of Brazil PCR -, and in Jiménez and Mencía (2009) - with data from the PCR operated by the Bank of Spain.

# **III. The role of PCRs in bank supervision**

Bank supervisors around the world typically adopt risk-based approaches to bank supervision. Supervision focuses on assessing banks particular vulnerabilities and the

> 14 Examples of countries using this approach are Argentina, Brazil, Costa Rica and Panama.

> 15 However, in response to the increasingly widespread development and use of rating systems by banks, regulators are recognizing those internal ratings to compute loan loss provisions. Hence, when banks report their information to the public credit registry they include the internal rating assigned to their obligors.

> 16 For example, banks’ rating systems must be very granular (i.e. have many rating grades). This is necessary to display discriminatory rating power. In turn, to construct such a granular rating system a wide array of information is required, both of the credit operations and of the borrowers themselves as well. This is particularly important when building rating systems for corporate obligors, who for example use information on liquidity and leverage ratios as inputs. This typically exceeds the range of information registered at public credit registries, which only collect information on the credits and associated collaterals.

> 17 Colombia and Mexico pioneered the development of these supervisory rating systems.

> 18 In particular, they provide useful information of a loan portfolio loss distribution such as its median, its mean (average loss rate, to be covered with total provisions) and different quantiles from the tail of the distribution, usually referred to as Value at Risk (VaR). Most importantly, the difference between the VaR, typically at the 99.9% confidence level, and the expected loss should be covered with capital.

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