1. Are Some Banks More Lenient in Implementation of Placement Classification Rules?* An Application of Dichotomous Rasch Model to Classification of Credit Risk in the Banking System Tomislav Ridzak, Financial Stability Department Croatian National Bank *The views expressed in this article are those of the author and do not necessarily represent the views of, and should not be attributed to the CNB

2. Motivation  Evaluation of credit risk in the portfolio is a key issue in bank management: Loss on a loan translates in to profit and loss and influences capitalization level through increased loan provisions If bad loans are not accounted for in a truthful manner, in the limit the bank stability is at stake  The loan classification is therefore important for bank management, depositors, owners and naturally regulators

3. Introduction  Loan classification in most countries involves substantial subjective judgement (World bank study by Laurin and Majnoni, 2003)  Inspecting the loan classification used by banks is difficult and costly (in terms of time and data)  This research compares the differences in placement classification of a common portfolio and obtains estimates of strictness / leniency for each bank

4. Related literature  Carey (2001) presents one of the first attempts to tackle the issue of consistency of banks’ ratings comparing ratings by different lenders to the same borrower  Hornik et al. (2007) use information from all possible bilateral comparisons and then detect outlying banks  Jacobson et al. (2005) use the sample of common borrowers rated by two banks and show there are substantial differences in the implied riskiness between the banks

5. Rasch model  Rasch model is used in order to obtain stricness / leniency estimate  The model was developed in order to separate measures of person ability (B) and item difficulty (D) in education research  It can be shown that the odds of a correct response by a person to one question, conditional on answering at least one of them is equal to difference between question difficulties

6. Rasch scores explained  The more able you are, higher the probability of getting the answer right

7. Bank leniency and application of the Rasch model  The credit risk classification is far from being a well established program with minimal human interaction  The Rasch model enables ranking of the banks according to their strictness by treating the banks as examiners and the companies as examinees  As a result the strictness / leniency estimate for each bank is obtained

8. Sample credit risk classified by number of company – bank links

9. Preparing the data  In the database the placements are divided in 3 major groups: A: extended to a reputable borrower with solid current and future cash flows or secured with adequate collateral B: probably will not be recovered fully C: no recovery is expected at all  Recoded as: 0 pays regulary 1 all other

11. Summary

13. Robustness  Testing for:  applicability of the model (does the data at hand fit to the Rasch model)  impact of collateral on estimated strictness/leniency estimate

14. 1. Item fit maps for Q4 of 2006 and 2007

15. 1. Item fit maps for Q4 of 2008 and 2009

16. 2. effect of collateral

17. Summary  The model gives an excellent way to aggregate available information about banks' approaches to classification of credit risk  The results are an excellent starting point for concentration of surveillance efforts  The results can also aid the assessment of financial stability of the banking system: they allow quick assessment of the risk management practices in the banking system