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 DepartmentCroatian 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

)()1( inn DBfxP

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

Number of companies

in sub sample

Credit risk in sub sample (HRK 000)

Share in total sub sample

Number of companies

in sub sample

Credit risk in sub sample (HRK 000)

Share in total sub sample

Number of companies

in sub sample

Credit risk in sub sample (HRK 000)

Share in total sub sample

Number of companies

in sub sample

Credit risk in sub sample (HRK 000)

Share in total sub sample

5 and more (banks with too few defaults excluded)

128 39,597,940 27.6% 85 36,207,466 26.6% 82 25,027,448 21.2% 67 16,961,416 16.4%

4 and more (banks with too few defaults excluded)

257 50,474,556 35.2% 187 45,245,584 33.2% 174 34,413,042 29.1% 146 29,578,468 28.6%

3 and more (banks with too few defaults excluded)

641 68,437,096 47.7% 514 60,625,207 44.5% 444 47,989,052 40.6% 421 43,103,946 41.7%

2 and more (banks with too few defaults excluded)

2,125 93,154,256 65.0% 1,821 86,285,779 63.3% 1,662 70,982,096 60.1% 1,549 63,794,361 61.7%

1 and more (banks with too few defaults excluded)

13,042 143,349,581 100.0% 12,000 136,252,638 100.0% 11,401 118,123,654 100.0% 10,404 103,472,125 100.0%

All firms and all banks 146,059,500 101.9% 148,704,568 109.1% 134,954,420 114.2% 108,624,662 105.0%

Memorandum items:

Firms with 3 and more banks / Total credit risk in the banking sector

20.8% 18.5% 16.3% 16.6%

All firms and all banks / Total credit risk in the banking sector

44.4% 45.3% 45.9% 41.9%

Credit risk in firms sorted by number of banks per firm

31/12/200631/12/200731/12/200831/12/2009

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

est. sig. low. CI upp. CI est. sig. low. CI upp. CI est. sig. low. CI upp. CI est. sig. low. CI upp. CI

Bank 1 1.14 ** 0.20 2.08 1.15 ** 0.30 2.01 1.29 ** 0.36 2.22 0.53 -0.11 1.17

Bank 2 0.37 -0.87 1.60 -0.71 -1.76 0.34 1.27 * 0.13 2.41 0.88 -0.47 2.23

Bank 3 0.98 -0.78 2.73 0.12 -0.95 1.18 -0.28 -1.32 0.76

Bank 4 0.42 -1.46 2.29 -0.73 -2.12 0.67

Bank 5

Bank 6 0.24 -0.88 1.37 0.04 -0.80 0.88

Bank 7 -3.47 ** -5.98 -0.97 -0.37 -2.58 1.84 -2.14 * -4.05 -0.22

Bank 8 -0.97 ** -1.67 -0.27 0.02 -0.80 0.83 0.30 -0.43 1.03 -0.40 -0.96 0.17

Bank 9 -2.54 ** -3.91 -1.17 -0.74 -2.84 1.37 0.07 -1.23 1.37 0.06 -0.97 1.08

Bank 10 -0.50 -1.17 0.17 0.14 -0.53 0.81 -0.42 -1.10 0.25 -1.58 ** -2.12 -1.05

Bank 11 1.34 -0.06 2.75 0.84 -0.86 2.55 0.08 -1.43 1.59

Bank 12 -0.65 -1.97 0.67

Bank 13

Bank 14 -5.07 ** -6.76 -3.38 -4.38 ** -6.15 -2.62 -2.57 ** -3.50 -1.63 -1.41 ** -2.41 -0.40

Bank 15 1.92 * 0.11 3.73 1.10 * 0.05 2.16

Bank 16 -1.03 -2.45 0.38 -0.95 -2.74 0.84 0.64 -0.52 1.80

Bank 17

Bank 18

Bank 19

Bank 20

Bank 21 1.97 -0.38 4.31 1.05 * 0.01 2.08

Bank 22 0.32 -0.63 1.28 0.19 -0.62 1.01 -0.18 -0.95 0.60 -0.09 -0.78 0.61

Bank 23 0.91 -0.37 2.19 0.82 -0.58 2.22 -0.82 -1.77 0.12

Bank 24 -0.53 -1.39 0.33 0.51 -0.46 1.47 -0.67 -1.47 0.13 -1.11 ** -1.75 -0.47

Bank 25 0.45 -0.72 1.62 0.75 -0.47 1.96 0.02 -1.04 1.08

Bank 26 1.47 ** 0.44 2.50 1.41 ** 0.42 2.40 0.59 -0.18 1.37 -1.34 ** -1.98 -0.70

Bank 27 -0.71 -2.32 0.91 0.34 -1.70 2.38 2.19 ** 0.75 3.62

Bank 28 0.90 -0.87 2.66 -0.53 -2.77 1.71 -0.84 -2.11 0.44

Bank 29

Bank 30 2.13 -0.05 4.31 1.29 ** 0.24 2.33

Bank 31 -0.80 -2.37 0.78 -0.89 -2.44 0.66 0.64 -1.48 2.77

Bank 32 1.33 -0.47 3.13

Bank 33 0.57 -0.56 1.70 2.72 ** 1.03 4.41 1.00 -0.34 2.34 0.91 -0.19 2.01

2006 2007 2008 2009

Summary

no. of banks assessed 19 19 17 24

total. no. of banks 33 33 33 32

no. of sig. <> 0 6 5 3 9

proportion 31.6% 26.3% 17.6% 37.5%

no. of strict 3 2 1 5

proportion 15.8% 10.5% 5.9% 20.8%

no. of lenient 3 3 2 4

proportion 15.8% 15.8% 11.8% 16.7%

2006 2007 2008 2009

est. sig. low. CI upp. CI

Bank 1 2.11 ** 1.16 3.06

Bank 2 2.46 ** 0.72 4.21

Bank 3 1.31 -0.04 2.65

Bank 4 0.86 -0.96 2.68

Bank 5

Bank 6 1.62 ** 0.50 2.74

Bank 7 -0.55 -2.97 1.87

Bank 8 1.18 ** 0.34 2.03

Bank 9 1.64 ** 0.28 3.00

Bank 10 0.00

Bank 11 1.66 -0.31 3.63

Bank 12

Bank 13

Bank 14 0.18 -1.17 1.52

Bank 15 2.69 ** 1.23 4.14

Bank 16 2.23 ** 0.68 3.77

Bank 17

Bank 18

Bank 19

Bank 20

Bank 21 2.63 ** 1.22 4.04

Bank 22 1.50 ** 0.53 2.46

Bank 23 0.76 -0.52 2.04

Bank 24 0.47 -0.36 1.30

Bank 25

Bank 26 0.24 -0.66 1.15

Bank 27 3.77 ** 1.88 5.66

Bank 28 0.75 -0.91 2.41

Bank 29

Bank 30 2.87 ** 1.45 4.29

Bank 31 2.23 -0.46 4.91

Bank 32 2.91 ** 0.60 5.22

Bank 33 2.50 ** 1.01 3.98

2009

no. of banks assessed 24

total. no. of banks 32

no. of sig. <> 0 13

proportion 54.2%

no. of strict 0

proportion 0.0%

no. of lenient 13

proportion 54.2%

2009

Robustness

Testing for:1. applicability of the model (does the data at hand fit to

the Rasch model)2. 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 2006

y = 0,001x - 0,0754

-6

-5

-4

-3

-2

-1

0

1

2

3

5 10 15 20 25 30 35

2009

y = -0,0194x + 0,2958

-6

-5

-4

-3

-2

-1

0

1

2

3

5 10 15 20 25 30 35

2007

y = -0,0251x + 0,4168

-6

-5

-4

-3

-2

-1

0

1

2

3

5 10 15 20 25 30 35

2008

y = -0,0239x + 0,4259

-6

-5

-4

-3

-2

-1

0

1

2

3

5 10 15 20 25 30 35

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