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

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

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

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

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

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

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

Sample credit risk classified by number of company – bank links

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

Summary

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

1. Item fit maps for Q4 of 2006 and 2007

1. Item fit maps for Q4 of 2008 and 2009

2. effect of collateral

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