alan flores - tdc sao paulo 20111017

7/28/2019 Alan Flores - TdC Sao Paulo 20111017

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Systemic estimation of PD, LGDand EAD for credit card as a

reserve requirement andvalidation method.

Mexican Experience


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Mexican Banking SystemThe Mexican banking system is concentrated in 7 banks that accumulate 87% of

the total loan portfolio and are mainly foreign owned (US, Spain, UK, Canada).

Jul-11 Aug-11 Jul-11 Aug-11 Jul-11 Aug-11 Jul-11 Aug-11

Banking System 2,271 2,298 2,510 2,542 12.53 12.32 16.39 16.21

BBVA Bancomer 581 588 620 634 20.65 19.95 15.58 15.51

Banamex 359 359 447 446 8.97 8.53 17.04 16.90

Banorte 271 275 288 282 14.59 14.23 15.94 15.67

Santander 297 301 316 338 15.77 15.61 14.81 14.24

Inbursa 153 155 123 120 8.48 8.21 24.82 23.21

HSBC 182 184 279 281 1.41 1.64 14.27 14.78

Scotiabank 114 115 121 121 10.20 10.04 16.60 16.43

Loan Portfolio Deposits

Balance sheet (Billions MXN)ROE (12m) (%) Capital Ratio (%)


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Mexican Banking SystemHowever the rate of deterioration of the credit card portfolio showed a

significant increase in 2009.

DelinquencyIndex (Past DueLoans / TotalBalance) of

consumer loansportfolio

Credit Cards

3.2%

9.8%

4.5%

2.7%

6.6%

1.4%0%1%2%3%4%5%

6%

7%8%9%

10%

Jan-05 Jan-06 Jan-07 Jan-08 Jan-09

ConsumerCredit

Other**

11%

Mortgage ConsumerCredit

CorporateCredit

Loan loss provisionAnnual Cash Flow

0

25,000

50,000

75,000

100,000

125,000

mar-06 sep-06 mar-07 sep-07 mar-08 sep-08 mar-090.0

1.0

2.0

3.0

4.0

5.0

** Includes Personal credit, leases, and other consumer credits


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Mexican Banking SystemThis deterioration was mostly explained by a systemic increase in household

indebtedness

Monthly Average Debt-Capacity per Debtor(Sample)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1 2 3 4 5 6 7 8

Number of Credit Cards

Available CreditLimit (left axis)

Dec. 2005 Nov 2007

MXN pesos

Number of debtors(right axis)

Credits per Person by Credit Type

3

3.2

3.4

3.6

3.8

4

4.2

4.4

D J F M A M J J A S O N D J F M A M J J A S O N

2005 2006 2007

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40

Bank credit cards (left axis) Mortgage (right axis) Car (right axis)

Source: Credit bureau


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G20 and Basel recommendations on

loan loss provisionsIn April 2009 the G20 issued recommendations on financial supervision and regulation that led

the Basel committee to propose the following recommendations related to loan loss

provisions:

1. Loan loss provisioning should be robust and based on sound methodologies that

reflect expected credit losses in the banks existing loan portfolio over the life of

the portfolio.

2. The accounting model for provisioning should allow early identification andrecognition of losses by incorporating a broader range of available credit

information than presently included in the incurred loss model.

3. The new standard should utilize approaches that draw from relevant

information in banks internal risk management and capital adequacy systems


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IASB and Basel recommendations on

loan loss provisionsRecommendations were sent to IASB in order to promote the necessary coordination between

standard setters, supervisors and regulators on their respective efforts to implement the G20

recommendations.

Consequently IASB issued in November 2009 a proposal to modify loan loss provisionsaccounting.

Incurred loss model (current IASB standard)

Assumes that loans will be paid until evidence on the contrary is identified (loss event).

The financial crisis has evidenced that these models are characterized by evaluating

optimistically the loan portfolio and are suddenly followed by large credit losses.

Expected loss models (proposal adopted by Basel and IASB)

Losses are estimated on a forward looking basis according to the quality of theportfolios. It implies an approximation of the PD, LGD and EAD.


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Content

1. Introduction

2. Systemic estimation of PD, LGD and EAD

3. Risk Analysis of the Credit Card Loans Portfolio

4. Conclusions


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Incurred LossesUntil August 2009, Mexico had an incurred loss type regulatory provisioning

model. Provisions were created by applying fixed percentages to loans in

different levels of delinquency.

Number of delinquent

periods

% of Provisions

0 2.5%

1 19%

2 48%

3 58%

4 62%

5 85%

6 95%

7 100%

8 100%

9 or more 100%


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Incurred Losses12 month write offs were significantly higher than the balance of loan loss

provisions revealed on banks balance sheets.

Q y C / C V

1 2 M E S E S

0 %

2 0 0 %

4 0 0 %

6 0 0 %

8 0 0 %

E ne -0 6 A g o -0 6 A b r-0 7 D ic -0 7 A g o -0 8

Write offs (next 12 months) / loan loss provision balance

Jan-06

Credit card overall portfolio

1 Write Offs in the following 12 months,.


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Incurred LossesSome disadvantages of the Incurred Loss Model are:

Loans are provisioned when factual evidence is available that

a loan or portfolio of loans will not be repaid in full. (Laterecognition of losses)

Generate provisions for no concrete time horizon.

Show pro-cyclicality since they generate largest amount ofprovisions when there is evidence that loans will not be

repaid.


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Incurred LossesSome disadvantages of the Incurred Loss Model are (continued):

Do not generate provisions for loans with no delinquency

even though these loans have a positive expected loss.

They do not consider the potential growth of the exposure atthe time of default (relevant feature in revolving credit).

Similar financial assets in different entities may generate losscoverage for different periods of time.


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Expected LossesInternal Models are characterized for estimating the components of the

expected loss.

Expected losses standardize the time horizon and the default definition

for all institutions.

Expected Loss = PD * LGD * EAD

Where:PD = Probability of default

LGD= Loss given default

EAD = Exposure at default


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PD estimationA window of 25 months was established to carry out the analysis of the

behavior "profile" of each credit card.

Historical Period (T-12, T0).

Performance Period (T0, T12)

Reference Point = T0

t= -12 t= 0 t= +12

Historical Period Performance

Period


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PD estimation

Default is declared when a borrower attains a past due status on his

payment obligations of 90 days.

t= -12 t= 0 t= +12

Performance Period


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Sample for analysisA sample of loan-level data representing 97.3% of total credit card loans in thesystem was extracted from banks.

# Contratos % SistemaInformacin

Recabada

% Informacin

Recabada

% Sistema

Ajustado

Inbursa 712,034 2.03% 712,034 2.03% 2.09%

AMEX 1,025,869 2.93% 1,025,869 2.93% 3.01%Banorte 1,256,316 3.59% 1,256,316 3.59% 3.69%

Santander 3,448,425 9.85% 3,448,425 9.85% 10.13%

Banamex 8,971,960 25.63% 8,971,960 25.63% 26.34%

BBVA Bancomer\Finanzia 13,999,809 40.00% 13,999,809 40.00% 41.11%

GE Money 796,019 2.27% 796,019 2.27% 2.34%

Invex 853,343 2.44% 853,343 2.44% 2.51%

Scotiabank 790,995 2.26% 790,995 2.26% 2.32%

HSBC 2,201,229 6.29% 2,201,229 6.29% 6.46%

Otros 946,902 2.71% - - -

TOTAL 35,002,901 100.00% 34,055,999 97.29% 100.00%

The sample allowed a maximum error of 40 basis points on a PD estimation with (1- ) =

99% confidence.

( ) > 1 dppP


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DatabaseData were organized in 12 windows of information spanning 25 months ofpayment history.

WINDOW FROM TO

Apr05

May05

Jun05

Jul05

Aug05

Sep05

Oct05

Nov05

Dec05

Jan05

Feb06

Mar06

Apr06

May06

Jun06

Jul06

Aug06

Sep06

Oct06

Nov06

Dec06

Jan07

Feb07

Mar07

Apr07

May07

Jun07

Jul07

Aug07

Sep07

Oct07

Nov07

Dec07

Jan08

Feb08

Mar08

1 Apr 05 Apr 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

2 May 05 May 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

3 Jun 05 Jun 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

4 Jul 05 Jul 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

5 Aug 05 Aug 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

6 Sep 05 Sep 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

7 Oct 05 Oct 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

8 Nov 05 Nov 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

9 Dec 05 Dec 07 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

10 Jan 06 Jan 08 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 1211 Feb 06 Feb 08 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

12 Mar 06 Mar 08 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12


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DatabaseThree sources of information were used for the analysis.

DATABASE 2Credit BureauInformation

DATABASE 1Bank Information

DATABASE 3Social securityInformation

t= -12 t= 0 t= +12


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PD

BorrowerBehavior

Product Policy CreditBureau

t= -12 t= 0 t= +12

Social securityinformation

10 Variables 46 Variables 9 Variables 10 Variables

Examples:

- Minimum payment amount of

the last 12 months.

-Maximum credit limit in the last

12 months.

-Theoretical time that will take

to repay the balance according

to the minimum payment and

the interest rate of the product.

Examles:

- Average use of the credit limit in

the last 12 months.

-Percentage of payment over the

balance.

- Number of times that the

borrower paid the total balance.

- Number of non-payments in the

credit card.

Examples:

- Number of credit cards

opened in the period.- Record of payment in other

open accounts

- Number of credits that the

borrower has at the reference

point.

Examples:

-The borrower has formal or

informal employment

- The borrower has a mortgage

with the Social Housing

institute (INFONAVIT)

- Borrowers Income at the

reference point (measured in

minimum wage).


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PDFor the estimation of the systemic PD standard logistic regression was used tocorrelate the historical period constructed variables with the observation

period binary event of default.

)...(1 1101

1),...,(

nnxxn exx

++++

=


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Percentage of paymentAmount of payment made by the cardholder as a proportion of the outstandingbalance at the reference point.

Credit Card Portfolio

Interval PD

0% 26.00%

10% 20.94%

20% 16.65%

30% 13.10%

40% 10.21%

50% 7.90%

60% 6.07%

70% 4.65%

80% 3.55%

90% 2.70%

100% 2.05%

110% 1.55%

% PMT (T0)

% default

Ave 0.3695

Std Dev 0.4051

Max 1.1000Q75 0.7109

Median 0.1464Q25 0.0557

Min 0.0000

Frecuency %

Default

%PMT (T0)

0%

20%

40%

60%

80%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Pmt = Payment

Bal = Balance

0

0)(%0

T

T

Bal

PmtTPMT =


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Credit Limit ConsumptionOutstanding balance of the credit card as a percentage of the credit limitoffered by the bank to the client.

Sistema

Interval PD

0% 5.67%

10% 6.75%20% 8.02%

30% 9.51%

40% 11.24%

50% 13.24%

60% 15.53%

70% 18.13%

80% 21.06%

90% 24.33%

100% 27.92%

250% 86.38%

% USE (T0)

% default

Ave 0.4851

Std Dev 0.3976

Max 2.5000

Q75 0.8657Median 0.4124

Q25 0.0984

Min 0.0000

Frecuency %

Default

% USE (T0)

0%

20%

40%

60%

80%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

250%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%35.0%

40.0%

45.0%


Bal = Balance

0

0)0(%T

T

LimitCredit

BalTUSE =


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Current Non-Payment (ACT)Consecutive periods, in which the cardholder has not covered the paymentobligation.

Sistema

Interval PD

0 9.24%

1 30.49%

2 65.39%

3 89.06%

ACT% Default

Ave 0.2386Std Dev 0.6219

Max 3.0000Q75 0.0000

Median 0.0000Q25 0.0000

Min 0.0000

Frecuency %

Default

ACT

0%

20%

40%

60%

80%

100%

0 1 2 30.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%



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Historical Non-Payment (HIS)Number of periods in which the cardholder has not covered the minimumpayment in the last 6 months.

Sistema

Interval PD

0 6.60%1 14.31%

2 28.30%

3 48.26%

4 68.79%

5 83.89%

6 92.49%

HIS

% Default

Ave 0.7226Std Dev 1.1467

Max 6.0000Q75 1.0000

Median 0.0000Q25 0.0000Min 0.0000

Frecuency %

Default

HIS

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%



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Type of EmploymentNon formal employment = 0

Formal employment (social security) = 1

Sistema

Interval PD

0 24.26%

1 10.77%

T_Employ

% Default

Ave 0.6227Std Dev 0.4847

Max 1.0000Q75 1.0000

Median 1.0000Q25 0.0000Min 0.0000

Frecuency %

Default

T_Employ

0%

20%

40%

60%

80%

100%

0 10.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%



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Income LevelNumber of times the minimum wage

Sistema

Interval PD

0 40.03%

1 33.08%

2 26.79%3 21.32%

4 16.71%

5 12.93%

10 3.20%

15 0.73%

20 0.16%

25 0.04%

30 0.01%

35 0.00%

Income

% default

5.4320

7.3905

116.91007.7700

2.6100

0.0000

AveStd Dev

Max

Q75Median

Q25

Min 0.0000

Frecuenc

y %Default

Income

0%

20%

40%

60%

80%

100%

[0,5

)

[5,1

0]

>10 0.0%

2.0%

4.0%

6.0%

8.0%

10.0%12.0%

14.0%

16.0%

18.0%

20.0%


0

0)(_ 0T

T

wageMinimum

IncomeTMWINCOME =


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Social Housing Institute BehaviorThe cardholder has (or does not have = 0) a Social Housing Institute mortgage.

Sistema

Interval PD

0 14.81%

1 17.25%

CREDIT (T0)

% Default

0.1510

0.3581

1.0000

0.0000

0.0000

0.0000

0.0000

Frecuency %

Default

CREDIT (T0)

0%

20%

40%

60%

80%

100%

0 10.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%


AveStd Dev

Max

Q75Median

Q25

Min

INFONAVIT is the Mexican Social Housing Institute (National Workers Housing Fund

Institute) that gives mortgage credits and deducts the monthly payment from the worker

salary.


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Maturity of the Credit Card in the Bank

Age of the credit card measured in months

Sistema

Interval PD

12 19.11%

24 16.87%

36 14.85%

48 13.03%

60 11.40%

72 9.95%

84 8.67%

96 7.54%

108 6.55%

120 5.68%

MAT (T0)

% Default

46.8745

64.1524

455.5333

48.7000

22.0000

10.80000.2667

Frecuency %

Default

MAT

0%

20%

40%

60%

80%

100%

12

24

36

48

60

72

84

96

108

120

>120 0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Meses


AveStd Dev

Max

Q75Median

Q25

Min


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Maturity in the Credit BureauAge on the credit bureau

Sistema

Interval PD

12 24.91%

24 22.96%

36 21.11%

48 19.38%

60 17.75%

72 16.24%

84 14.83%

96 13.52%

108 12.31%

120 11.20%

MAT_CB (T0)

% Default

91.2057

60.4804

455.5333

129.7333

81.8000

44.4000

0.9000

Frecuency %

Default

MAT_CB (T0)

0%

20%

40%

60%

80%

100%

12

24

36

48

60

72

84

96

1

08

1

20

>1

20

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Meses


Ave

Std DevMax

Q75

Median

Q25

Min


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Open AccountsNumber of existing credit cards at the reference point (Open credit cards, notclosed before the reference point).

Sistema

Interval PD0 13.61%

1 13.82%

2 14.03%

3 14.24%

4 14.46%

5 14.68%

6 14.91%

7 15.13%

8 15.36%

9 15.59%10 15.83%

ACCOUNTS TOT% Default

6.9802

5.057584.0000

9.00006.0000

3.0000

0.0000

Frecuency %

Default

ACCOUNTS_TOT

0%

20%

40%

60%

80%

100%

0 1 2 3 4

5ym

s 0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%45.0%


AveStd Dev

Max

Q75Median

Q25

Min


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Delinquency in the Credit BureauIndicates if the cardholder presented in the previous 12 months delinquency onany other debt obligation different than the credit card.

Sistema

Interval PD

0 6.59%

1 24.89%

DELINQ_CB_HIST

% Default

0.4754

0.4994

1.0000

1.0000

0.00000.0000

0.0000

Frecuency %

Default

DELINQ_CB_HIST

0%

20%

40%

60%

80%

100%

0 1 0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%


AveStd Dev

Max

Q75Median

Q25

Min


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PDSelected Variables

The selected variables in the model are:

Current Non-Payment (ACT): Number of consecutive periods, up to the reference

point, in which the cardholder has not covered the minimum payment.

Historical Non-Payment (HIS): Number of periods in which the cardholder has not

covered the minimum payment in the last 6 months.

Percentage of payment (% PAY): Amount of payments made by the cardholder

over the total balance at the reference point.

Credit Limit Use (% USE): Percentage that represents the total balance from the

credit limit at the reference point.

Maturity (MAT): Number of months elapsed since the opening of the credit card

to the reference point.


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Average Cardholder

The average cardholder of the Credit Card Loans Portfolio has the followingvalue for each selected variable in the model.

Credit CardPortfolio

Actual Non-Payment (ACT) 0.24

Historical Non-Payment (HIS) 0.72

Maturity (MAT) 46.87

Percentage of payment (%PAY) 36.95%

Credit Limit Use (%USE) 48.51%


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PD

Final Estimation of PD

Receiver operating characteristic ROC = 86%

Estimator Description Value

Constant -2.9704

C1 ACT +0.6730

C2 HIS +0.4696

C3 MAT -0.0075

C4 % PAY -1.0217

C5 % USE +1.1513

INCUMP_90D_4 / Coeficientes estandarizados(Int. de conf. 95%)

MORAMIN_SA

MORAMIN_HIS

ANTIG_T0 PJE_PAGO_T0

PUSO_LINEA_T0

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Variable

Coeficientes

estandarizados

)*1513.1*0217.1*0075.0*4696.0*6730.09704.2( 54321

1

1CCCCC

e

++++

=)

Standardized Coefficients


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Parameters Estimation

Loss GivenDefault

(LGD)


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LGD

3 months

Frequency of Classes

The Loss Given Default was established at 81%.

% Payment % ClassAverage

Payment% Recovery

3 months


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Estimation of Parameters

Exposure atDefault

(EAD)


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EAD

Credit limit use at the time of analysis (horizontal axis) and the final exposure atthe time of default (vertical axis)

0%

500%

1000%

1500%

2000%

2500%

3000%

0% 50% 100% 150% 200% 250% 300%

PUSO_LINEA_T0

F

actor=EAD/Saldo_

T0

Exposureatdefault/

balancetoday

%USE: Percentage that represents

the balance at the reference point

from the credit limit.

Factor: Exposure at the date of

default over the balance at the

reference point.

Balance_T0: Balance at the point of

analysis (date of reference).

%USE = Balance today / Credit Limit

%USE is the same variable used to estimate PD


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EAD

An EAD adjustment factor is deducted from data.

Factor

0% 5% 845%

5% 10% 447%

10% 15% 333%

15% 20% 274%

20% 25% 237%

25% 30% 211%

30% 35% 192%

35% 40% 176%

40% 45% 164%

45% 50% 154%

50% 55% 145%

55% 60% 138%

60% 65% 131%

65% 70% 126%

70% 75% 120%75% 80% 116%

80% 85% 112%

85% 90% 108%

90% 95% 105%

95% 100% 101%

100% 105% 100%

105% 110% 100%

110% 115% 100%

115% 120% 100%

%USE

EAD = Balance_T0* Factor

0%

100%

200%

300%

400%500%

600%

700%

800%

900%

1000%

0% 20% 40% 60% 80% 100% 120% 140%

% USE

Fac

tor

{ }%100,*0_ 5784.05CMaxTBalance

C5 = Credit Limit Use (Balance_T0 / Credit Limit) at the reference point.

%USE is the same variable used to estimate PD


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Final Proposal

Current regulation for credit card reserve. Entered into effect september 2009

Credit Card Provisionsi = PDi * LGD * EADi

Where:

=

+< +++

%1004

1

14

1

)5*1513.14*0217.13*0075.02*4696.01*6730.09704.2(1

CSi

eCSi CCCCC

PD

C1 = Number of consecutive periods in which the cardholder didnt cover the minimum payment at the reference point

C2 = Number of periods in which the cardholder did not cover the minimum payment in the last 6 months

C3 = Maturity of the credit card in the Institution at the reference point (months)

C4 = Amount of payment made by the cardholder over the outstanding balance at the reference point

C5 = Percentage of the outstanding balance at the reference point over the credit limit.

%75=LGD

{ }%100,*0_ 5784.05 CMaxTBalanceEAD =


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Content

1. Introduction

2. Provisions Based on Expected Loss for a Revolving Credit Portfolio


4. Conclusions


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Credit Card Loans Portfolio

Interest SpreadThe formula allowed for loan level expected loss estimation. Expected loss iscompared to interest rate.

5%

10%

15%

20%

25%

30%

35%

40%

Sep-05 Dec-05 Mar-06 Jun-06 Sep-06 Dec-06 Mar-07 Jun-07 Sep-07 De-07 Mar-08

Expected Losses Spread

Expected Losses were estimated for the Mexican credit card loans portfolio using the presented model for provisions.


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Spreads vs Expected LossesBased on the risk-return relationship found for each bank, 4 types of

institutions were identified.

0%

10%

20%

30%

40%

50%

60%

0% 10% 20% 30% 40% 50% 60%

Spread

WELL

ESTABLISHED

MULTISEGMENT(low & high

incokme)

COMPETE IN NEW SECTOR

PRICECOMPETITION

InsufficientPrice

Over-Priced

Expected Losses


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Maturity and Expected LossesMore recent clients to the financial sector are riskier.

The size of thecircle indicates theexpected loss

EL %

0

20

40

60

80

100

120

0 20 40 60 80 100 120Maturity in the Financial System (Credit Bureau first record) (months)

Average

MaturityintheBank

(months)

LOYAL CREDITCARDHOLDERS

HIGHCOMPETITION

PRICECOMPETITIONOLD USERS

CLIENTS THAT HAVENEVER HAD ACREDIT CARDBEFORE

1/ Expected Loss is the average of the expected losses for the period between April 2006 and March 2007


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IRB model validation

PD CNBV vs PD Bank X

Modelo PD CNBV

IRB

PD CNBV vs PD BankY

PD Model IRB

PD CNBV

Observed default rate


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Interest rate differentation according to riskcharacteristics of clients

*/ Ex-post (in the next 12 months after the horizontal axis computation)

Outstanding balance / Credit limit

Payment / outstanding balance


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PD Through the Cycle vs PD Point in Time


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Content

1. Introduction

2. Provisions Based on Expected Loss for a Revolving Credit Portfolio


4. Conclusions


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Credit Card Loans PortfolioThe impact in the system was significant (average 2.14 times the previous

requirement).

0%

10%

20%

30%

40%

50%

60%

70%

BANK 1 BANK 2 BANK 3 BANK 4 BANK 5 BANK 6 BANK 7 BAN K 8 BANK 9 BANK 10 SISTEMA

Incurred Loss Expected Loss

Institution

Expected Loss

vs Incurred

Loss

BANK 1 2.13x

BANK 2 1.8x

BANK 3 1.75x

BANK 4 2.14x

BANK 5 2.61x

BANK 6 2.53x

BANK 7 2.26x

BANK 8 3.25x

BANK 9 1.56x

BANK 10 2.02x

PORTFOLIO 2.14x


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Capital Requirement and ProvisionsCredit Card Portfolio

Basel I & incurred lossprotection

Expected Loss

18.4%

BIS II (1-k=0.001)36.5%

Provisions (Incurred Loss)9.3%

BIS I (1-k=0.6487)17.3%

9.3%

18.12 %

% portfolio

FREC

UENCY

18.4%

36.5%

17.3%

8.%

Basel II & expectedloss protection


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Individual bank estimation

Best bank

Expected loss13.70%

BIS II (1-k=0.001)27.49 %

Reserves8.10%

BIS I (1-k=0.6689)16.10%

8% cartera

13.79% cartera


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Individual bank estimation

Worst bank

Expected loss30.21%

BIS II (1-k=0.001)54.11%

Reserves15.84%

BIS I (1-k=0.6689)23.84%

8% cartera

23.90% cartera


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Systemic PD, EAD, LGD as validationmethod

In Mexico no bank had an internal model before the introduction of this rule

because the incentive of capital reduction was the opposite.

This method does not intend to substitute internal models. On the contrary it seeks

to incentivize its use and development by setting a comparable standard as the

standard method of reserves.

One bank has certified its internal model in parallel with the introduction of this

rule. Loan level comparisons were done and general parameters compared for

sample portfolio. Differences in parameters were modest and subject to

explanation.

This approach allows a rich validation process as specific differences on PD

estimation can be explained by bank specific policies which are made evident by

comparing parameter estimates.


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Conclusions

The National Banking and Securities Commission (CNBV) is gradually changing theregulation from an incurred loss to an expected loss provisioning scheme

The first portfolio that changed was the revolving consumer loans (credit cards)

which is now being provisioned with the presented expected loss model. In March

2011 mortgage and personal loans were introduced. September 2011 saw the stateand municipalities reserve rule change and december 2011 is the objective date for

corporate and SMEs loans (D&B score).

The objectives of these reforms are to recognize losses in a timely manner, to

assure that provisions cover losses for a 12 month horizon, to apply international

standards and encourage banks to use more information in the process.

This type of models also represent an incentive for the banks to develop their

internal rating models for provisioning and capital assessment.


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Systemic estimation of PD, LGD

and EAD for credit card as areserve requirement and

validation method.

Mexican Experience

alan flores - tdc sao paulo 20111017

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