Basel Committee (Basel Committee on Banking Supervision, 2004 and 2005). The World Bank Group also uses credit scoring approaches to rate sovereign borrowers, counterparties in financial derivative transactions, investment targets, and private sector borrowers.

**Table 2.1: Moody's Investor Service score card for regulated electric and gas utilities**<sup>**17**</sup>

|**Broad Rating Factor**<br>**Broad Rating**<br>**Factor Weighting**<br>**Factor / Sub**|**Rating Sub-Factor**<br>**- Factor Weighting - Regulated Utilities**|**Sub-Factor**<br>**Weighting**|
|---|---|---|
|Regulatory Framework<br>25%|Legislative and Judicial Underpinnings of the Regulatory<br>Framework|12.5%|
||Consistencyand Predictabilityof Regulation|12.5%|
|Ability to Recover Costs<br>and Earn Returns<br>25%|Timeliness of Recovery of Operating and Capital Costs|12.5%|
||Sufficiencyof Rates and Returns|12.5%|
|Diversification<br>10%|Market Position|5%|
||Generation and Fuel Diversity|5%|
|Financial Strength, Key<br>Financial Metrics<br>40%|CFO pre-WC + Interest / Interest|7.5%|
||CFOpre-WC / Debt|15%|
||CFOpre-WC - Dividends / Debt|10%|
||Debt/Capitalization|7.5%|
|Total<br>100%||100%|
|NotchingAdjustment|HoldingCompanyStructural Subordination|0 to -3|

_Source_ : Moody’s Investor Service, 2013.

Score cards offer a flexible approach to conduct credit risk analysis since they can be tailored to reflect the specific risk exposure and facilitate a standardized evaluation of credit risk among entities in the same industry. However, developing scorecards and scoring guidelines requires a deep understanding of industry-specific risk drivers. Since scoring reflects credit analysts’ subjective assessments, guidelines that prescribe scores for specific outcomes of the assessed credit metrics can be used improve objectivity. Credit committees that review analysts’ ratings may also help to improve objectivity.

# **_Statistical models_**

In statistical models, credit quality is measured using observable characteristics (e.g. financial ratios, competitive position, regulatory environment, etc.). A statistical model is estimated regressing historic outcomes (e.g. defaults) on historic characteristics. The output from a statistical model is a measure of the likelihood that a credit event will occur (default probability).

Edward Altman (1968) pioneered a statistical model using Z-scores to predict corporate defaults. To calibrate the model Altman chose financial ratios that most significantly drove bankruptcy in his data set (as shown in Figure 2.2.).<sup>18</sup> Turkish Treasury’s credit rating approach described below is based on a statistical model.

> 17 Moody’s Investor Service 2013 provides a detailed rationale for the inclusion and definition of the various rating (sub-)factors included in this score card. This rating methodology paper also provides detailed scoring guidance for each rating (sub-) factor. Rating (sub-) factors shown in this score card are specific to utilities. Score cards for entities in other sectors would show different (sub-) factors. E.g. fuel diversity in the generation of power (such as coal, oil, gas, etc.) is a highly sector-specific driver of credit quality. CFO stands for cash flow from operating activities. WC stands for working capital.

> 18 The Risk Metrics Group developed a commercial product based on Altman’s Z-score methodology (Altman, et al., 2010)

10 | P a g e