Basel from a Business Perspective

Basel From a Business Perspective
Abstract: This paper discusses the main features of the Basel capital accord with focus on the retail portfolio
and provides unique ideas covering the following:
• Introduction to Basel and Credit risk
• Concept of Probability of default, Loss given default and Exposure at default
• Basel importance for the Business managers
• Basel as an enabler for the Fintech lending approach along with the Bureau Data
• IFRS9 background and comparison with Basel
• Modeling Techniques for Probability of default and Loss given default
• Major guideline changes between Basel II and Basel III
October 2017

Introduction to Basel
Modeling concepts
Basel for day to day business
Basel vs. IFR S9
2

Basel is an international business standard that requires financial institutions to maintain enough capital reserves to cover risks
incurred by operations. The Basel accords are a series of recommendations on banking laws and regulations issued by the Basel
Committee on Banking Supervision (BSBS). The name for the accords is derived from Basel, Switzerland, where the committee
that maintains the accords meets.
Basel is based on three pillars:
Pillar 1: Minimum Capital Requirement / Risk Assessment.
Pillar 2: Supervisory Review Process / Risk Management.
Pillar 3: Market Discipline / Declaration
Risk Assessment, Risk Management and Declaration are the names suggested by the Authors
3

Introduction to Basel- The Three Pillars
Market discipline supplements regulation as sharing of information facilitates assessment of the bank by others
including investors, analysts, customers, other banks and rating agencies.
Deals with maintenance of regulatory capital calculated for three major components:
Credit risk
Operational risk
Market risk
Deals with maintenance of regulatory capital calculated for three major components:
The process by which institutions assess the adequacy of their capital. This process is sometimes referred to as
the Internal Capital Adequacy Assessment Process (ICAAP). The ICAAP is based on a solid risk management and
control framework.
The assessment of this process by the supervisor. This assessment is generally referred to as the Supervisory
Review and Evaluation Process (SREP). The SREP also includes checking compliance with minimum solvency
requirements and requirements on internal control.
Dialogue between the institution and the supervisor on capital adequacy.
Basel guidelines are framework to protect the international financial system from the problems that might arise from a major bank or a series of banks collapse.
4

Risk Assessment- The First Pillar
The following risks need to be covered under Basel guidelines for capital calculations:
Risk related with obligor for the following:
Ability to repay: E.g. Obligor
defaulted due to income close to
monthly EMI.
Willingness to repay : E.g. Obligor
behavior pre and post marriage or
certain age groups
Risk that the value of an investment will
decrease due to moves in market factors. It is
irrespective of the credit quality of the obligor.
E.g. Obligor provided shares as
collateral and market collapsed –
collateral don’t value the same as at
the time of loan approval
Risk of loss resulting from inadequate or failed
processes, people and systems or from
external events.
E.g. No address proof was taken
from the obligor while sanctioning
the loan.
Credit Risk is the most important of all three categories:
• Obligor level initiatives and credit quality is considered only in credit risk – therefore it is the most complicated risk assessment part in pillar1
• Credit risk unlike market and operational varies for obligor ,asset type, and lending rules
• Most of the Basel accord provides guidelines for credit risk assessment, medium size section on market risk and few pages on operational risk
• Credit risk for the retail portfolio will be discussed in detail in the coming sections
5

Minimum Capital Requirements
Risk weighted assets (RWA) are calculated for all three risks to assess the accurate extent of risk. RWA – is a bank’s assets or loans provided to
obligors weighted according to risk.
Capital requirement calculations are conducted by assessing all three risks to manage.
Expected Losses: will be managed through risk based pricing
Unexpected Losses: will be managed through maintaining reserves
RWA is computed directly for Credit risk only under Standardized Approach
Market and Operational Risk don’t estimate risk at asset or obligor level therefore doesn’t have direct RWA estimation
RWA is calculated as 12.5 of the capital requirements wherever it cannot be computed directly
* Risk weighted assets (RWA) are computed as 12.5 X Capital Requirement.
6

Credit Risk
Standardized:
Standardized
Foundation/Advanced Internal Rating (FIRB/AIRB)
Sovereigns
Corporates- SME, LE, Specialized Lending
Retail – Personal Loan/Credit Card/Auto Loan
Retail – Residential
Overdraft
Cash
Other Securities
Other Assets
Under this approach the banks are required to use ratings from External Credit Rating Agencies to quantify required risk weighted and in turn
capital for credit risk. In this approach banks group their assets into the following segments, list in not exhaustive:
Risk weights are provided in the Basel Accord for each rating for all the segments
As we can see it is a simple and crude method to estimate the capital requirements. Here, quality lending initiatives insured by banks are
overlooked or not considered at all.
This is the only approach where risk weighted assets are calculated – no differentiation in Expected and Unexpected losses
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7

Credit Risk- Foundation/Advanced Internal Rating
Foundation/Advanced Internal Rating Based Approach:
As Bank moves from Standardized approach to FIRB and AIRB, the lesser capital reserves bank needs to keep with them with same lending structure.
FIRB uses internal data for PD estimation, EAD and LGD are used directly from Basel guidelines.
AIRB uses internal data for all the three matrices i.e. PD, EAD and LGD estimation.
In this document, we are covering PD, LGD and EAD estimation techniques.
While it is never possible to know in advance the losses a bank will suffer in a particular year, a bank can forecast the average level of credit
losses it can reasonably expect to experience. These losses are referred to as Expected Losses (EL)
Losses above expected levels are usually referred to as Unexpected Losses (UL) – institutions know they will occur now and then, but they
cannot know in advance their timing or severity.
In this approach, institutions will be allowed to use their own internal measures for key drivers of credit risk as primary inputs to calculate capital
requirements by means of risk weight formulas specified by the Basel Committee.
In the credit business, losses of interest and principal occur all the time - there are always some borrowers that default on their obligations. The
losses that are actually experienced in a particular year vary from year to year, depending on the number and severity of default events, even if
we assume that the quality of the portfolio is consistent over time
Probability of default (PD) per rating grade, which gives the average percentage of obligors that default in this rating grade in the course of one
year.
Exposure at default (EAD), which gives an estimate of the amount outstanding (drawn amounts plus likely future draw downs of yet undrawn lines)
in case the borrower defaults.
Loss given default (LGD), which gives the percentage of exposure the bank might lose in case the borrower defaults.
Three major matrices required to estimate the losses:
Capital Estimation through IRB –Basel Risk Formula
Capital requirement (K) = [LGD * N [(1 - R)^-0.5 * G (PD) + (R / (1 - R))^0.5 * G (0.999)]
- PD * LGD] * (1 - 1.5 x b(PD))^ -1 × (1 + (M - 2.5) * b (PD) X EAD
8

PD, LGD and EAD: Generic Concepts
Probability of default (PD) per rating grade, gives the
average percentage of customers that default in
this rating grade in the course of one year.
PD EAD
Probability of default
estimates the chances of
customer going bad in
the future (for a defined
period of time).
EAD estimates the
outstanding balance with
which customer will go
bad in the future.
LGD
LGD estimates the loss
percentage to the
outstanding Balance
when the customer
defaults/bad.
The loss of principal
The carrying costs of non-
performing loans, e.g.
interest income foregone
Workout expenses
(collections, legal, etc.)
Default definition: it can be decided by roll
rate analysis, Basel recommended definition is
90+ DPD.
Outcome window/Time Horizon: it can be
decided by vintage analysis, Basel
recommended window is 12 months.
t
n n+t
Current time =
n: non default
here
Current time =
n: non default
here
Future time = n
+ t: defaulting
between n+1 to
n+t
Future time = n
+ t: defaulting
between n+1 to
n+t
9

Modeling concepts
Basel vs. IFR S9
10

PD Modeling Concept
To estimate PD, customers will be tracked for the next 12 months from the point of observation and their tracked
behavior will be estimated through modeling exercise.
Prediction Time Window = 12 MonthsPrediction Time Window = 12 Months
Observation Point:
31 Aug 2016
Prediction Window End
Point: 1st
Sep 2017
Accounts which are in stage1
will be selected at this point.
Accounts which are in stage1
will be selected at this point.
Analysis to be
done
Analysis to be
done
Objective is to identify,
how many accounts have
moved from performing
to Default Stage (90+
DPD)
Here Stage1
(Performing)
How many are in
Stage 3 (90+ DPD)?
Accounts worst performance
until Sep 2017 will be
assessed to estimate the bad
rate which will be predicted
through modeling.
Accounts worst performance
until Sep 2017 will be
assessed to estimate the bad
rate which will be predicted
through modeling.
Analysis to be
done
Analysis to be
done
11

PD Modeling
The probability of default is an estimate of the likelihood that the default event will occur. It applies to a particular assessment horizon, usually one
year.
To develop PD models the following needs to be established to perform above mentioned analysis:
Bad Definition: It has the following components
Time in delinquency: Roll rate analysis is used to define the time in delinquency -30 DPD, 60 DPD, 90 DPD etc. Roll rate analysis is an approach to
track the movement of accounts between various risk buckets
Performance window: Month on book from the instrument start date to track the default event- 3, 6, 9, 12 months etc. Best industry practice is to
identify the number of months on book through vintage analysis after which cumulative delinquency rates are not increasing or increasing at a very
nominal rate compared to the earlier months
Defining the default event: As per Basel guidelines it is 90 DPD for retail portfolios
Roll rate Analysis: In the credit card industry, the "roll rate" is the rate at which 30-day delinquencies "roll" to become 60-day and then 90+ day
delinquencies and vice versa.
12

PD Modeling
Three major techniques are used to develop PD models:
Application Scorecards : for fresh obligors through application and bureau data. KGB, UKGB models and reject Inference
Behavioral Scorecards- For existing obligors with more than 6 months on books through behavioral, application and bureau data
Rating Models – for corporate portfolios and specialized obligor (e.g. project finance) through Ledger, P&L Account and balance sheet
In this document we have covered only Behavioral Scorecards development process:
PD model development
Target Definition based on roll rate and prediction window : 1 or 0 for 90+ DPD for next 12 months
Observation window: usually six months but also depends on the variables
13

PD Modeling Fine and Coarse Classing
Target Variable and Explanatory Variables:
Target variable: will be the event which we want to predict through the modeling. E.g. Customer going 90 + DPD in next 12 months will be
the target, customer with less than 30 DPD will be the non target, customers with 30 – 89 DPD will be the exclusions. Modeling will be done on
target and non target population/
Explanatory Variables: will be the variables which will predict or explain the target variable behavior. E.g. Age can predict the default,
customer with age 35 years or above has significantly less default rate than customers with age less than 35 years
Coarse Classing from Fine Classing
All the explanatory variables are compared in terms of their strength to predict the target variable through Weight of Evidence (WOE)
and Information value (IV) in the Fine and Coarse Classing
WOE: measures the strength of the explanatory variable at the group level to separate between target and non-target event. Higher the value
(negative or positive), higher the predictive power of the group
WOE = Log (% Non Target/ % Target )
Information Value (IV) :is the sum of the (absolute) values of WOE for all the groups, shows the strength of the characteristic’s ability to predict
risk. Higher the value , higher the predictive power of the overall variable
IV = (Non Target – Target) X Log (% Non Target/ % Target ) totaled for all the groups
14

PD Modeling Fine and Coarse Classing
Fine classing is making fine/large number of groups for the
explanatory variable to assess the predictive power as
shown in the following table
Age
Bands
(A) %
Total
(B)%
Target
Overall
(C) % Non-
Target
Overall
(D) %
Target in
the
Group
(E) % Non-
Target in
the Group
(F)
Weight of
Evidence -
WOE (G) IV
20-22 7.0% 16.3% 6.5% 11.7% 88.3% -0.92 0.09
23-25 12.8% 24.7% 12.2% 9.6% 90.4% -0.71 0.09
26-30 11.6% 13.0% 11.6% 5.6% 94.4% -0.12 0.00
31-35 11.6% 12.0% 11.6% 5.2% 94.8% -0.03 0.00
36-40 14.0% 14.4% 13.9% 5.2% 94.8% -0.03 0.00
41-45 13.4% 8.4% 13.6% 3.1% 96.9% 0.49 0.03
46-50 9.3% 7.0% 9.4% 3.8% 96.3% 0.30 0.01
51-55 8.7% 2.8% 9.0% 1.6% 98.4% 1.17 0.07
56-65 5.8% 0.6% 6.1% 0.5% 99.5% 2.32 0.13
66-80 3.5% 0.6% 3.6% 0.9% 99.1% 1.80 0.05
81-95 2.3% 0.3% 2.4% 0.6% 99.4% 2.09 0.04
Total 100.0% 100.0% 100.0% 0.51
Coarse classing: Based on the trends in the fine classing, characteristic is
grouped again into the stronger/coarse classing as shown below
Age
Bands
(A) %
Total
(B)%
Target
Overall
(C) % Non-
Target
Overall
(D) %
Target in
the
Group
(E) % Non-
Target in
the Group
(F)
Weight of
Evidence -
WOE (G) IV
20-25 19.8% 40.9% 18.7% 11.0% 89.0% -0.79 0.18
26-40 37.2% 39.4% 37.1% 5.4% 94.6% -0.06 0.00
41-50 22.7% 15.4% 23.1% 3.1% 96.9% 0.41 0.03
51-55 8.7% 2.8% 9.0% 1.6% 98.4% 1.17 0.07
56 + 11.6% 1.5% 12.2% 0.6% 99.4% 2.09 0.22
Total 100.0% 100.0% 100.0% 0.50
15
Note: IV can drop from fine classing to coarse classing but it is required to get a more stable prediction through combining similar attributes, e.g.
group above age 51 are combined together.
Weight of evidence is the value which will go into logistic regression – For e.g. all obligors with age from 26 to 40 years will have same value
i.e. -0.06. Therefore, age variable for all obligors in this example will have only 5 values for the logistic regression.
Similar grouping will be done for all the important predictive variables.

PD Modeling Output
Next Step
Logistic regression can be used to select the most predictive variables for the scorecard.
Important measures required to understand the modeling output.
Variance inflation factor (VIF) is assessed to avoid the variables with high correlation
among themselves (using predictive characteristics in the model which contains similar
information)in the model . E.g. Number of Dependents and Marital Status, Age and
length of Bureau history etc. can be highly correlated. Multi-collinearity can make the
model estimates biased and unstable.
VIF for all the selected variables should be less than 2 to avoid prediction based on the
repetitive information/multi co linearity
P value estimates the probability of the characteristic impacting the event which model
is trying to predict, lower the P value higher the probability of impacting the event. P
value is used to select the final characteristics which will go into the model.
Wald Chi estimates the contribution of the characteristic in impacting the event
compared to the other characteristics. Wald chi is used to assess the contribution range
for the selected characteristics.
It is possible that P value will be same for the selected characteristics but Wald Chi will
be used to identify their individual contribution to the model
Model variables importance can be determined by looking P value and Wald chi
statistics based on the following best practices:
P value for retail portfolio for selected variables should be less than .0001%
% Wald Chi contribution for a particular variable should be between 5 –35 %
Note – Estimated relationship between target event and characteristic should align with the business sense. E.g. if model is suggesting that
customers with longer bureau history and no delinquency across the market in the recent months have higher default rates, in that case
further investigation is required to understand this trend. 16

PD Modeling Results
Following model parameters can be used to assess the model quality and robustness
Gini: captures the overall separation power of model between target and non-target
KS: maximum separation between target and non-target
Rank ordering: higher score/rank bands should have higher number of defaulters
Average capture rate: higher rank bands should have high capture rate
Gini
KSRank Ordering
Average Capture
17

PIT PD corresponds to the usual meaning of “probability of default” and is, in fact, conditional to the existing credit cycle.
TTC PDs, in contrast, reflect circumstances anticipated over an extremely long period in which effects of the credit cycle
would average close to zero.
PD Modeling Results
Point in time PD and Through the cycle PD
Why TTC PDs are used in the regulatory capital calculation instead of PIT PD:
Stability is seen as a desirable attribute of a strategic capital reserve and some regulators have expressed concerns that if banks were to use
PIT measures, they might overload their balance sheets during peak periods prior to an economic downturn
PIT adjustments entail large costs, the range of acceptable values of risk per unit of capital would be large and so adjustments would occur
rarely
Drawback
Stability in the TTC indicators is artificial, caused by much of the information on the current situation being hidden until the belated
release of an annual statement.
Therefore bank needs both PIT PD and TTC PD
TTC PD Modeling
Converting PIT DD (default distance) to TTC PD – when portfolio have seen half or full credit cycle
TTC PD through macroeconomic indicators – Back track modeling of PIT DD with help of macroeconomic indicators
18

LGD Modeling Concept
LGD is defined as the ratio of losses to the exposure at default.
The LGD calculation is easily understood with the help of an example: If the client defaults, with an outstanding balance of $200,000
(exposure (EAD)) and the financial institution is able to collect $160,000. Cost for the collection activity is $10,000.
Loss = $200,000—$160,000 + $10,000 = $50,000
LGD = Loss /EAD = $50,000/ $200,000 =25%
Recovery Already Incurred Recovery
Expected
Total Recovery =
Recovery Already
Incurred + Recovery
Expected
Bal 1/Bal
at Default
Bal2 Bal3 Bal4/Current
Balance
Bal 6 Bal 7 Bal8
Total Recovery Window
Time when
customer
entered into
default (90 +
DPD)
End of the
period, post that
no recovery is
expected
LGD for non-Default
(< 90 DPD)
Customers
= 1 – Total Recovery/ Balance
at Default
LGD for
Defaulted(>=90
DPD) Customers
= 1 – Recovery
Expected/Current Balance
LGD1
LGD2
Illustration
Purpose Only
19

LGD Modeling
LGD Modeling
LGD is a common parameter in Risk Models and also a parameter used in the calculation of Economic Capital or Regulatory Capital under
Basel. LGD is usually defined as the ratio of losses to exposure at default and cover three major categories of losses:
The loss of principal
The carrying costs of non-performing loans, e.g. interest income
foregone
Workout expenses (collections, legal, etc.)
LGD1: will be applied to the performing book. Accounts who have not defaulted yet – if defaults the losses bank need to cover is captured
under LGD1
LGD2: will be applied to the non-performing book. Accounts already defaulted –the losses bank need to cover is captured under LGD2
The following techniques can be used to develop LGD models:
Run off Triangle technique
Survival technique
Two stage modeling through Inverse mills ratio
20

LGD Modeling
This methodology uses observed and forecasted monthly recoveries to estimate the total recovery amount.
This methodology firstly develops, aggregated recovery data at a vintage level (a vintage is defined here as the month of transfer to the Default
book) of defaulted accounts and the payments received during a given month are summed for each vintage.
This will give us a square matrix which size equals the total number of available vintages and where only the top left hand corner is filled with all the
monthly observed recovery payments.
Now, we develop another data set with the help of previously developed aggregated data set which will not only have the observed recoveries but
will also have the predicted recoveries for all the available vintages for a given/fixed duration. This fixed duration will be estimated by analyzing the
internal data.
The methodology to develop forecasted recoveries is largely inspired from a well-known actuarial method which searches for a certain development
pattern to forecast future recoveries (in numbers or values) of a run-off triangle. This methodology is an absolute fundamental in the insurance
world as it provides one of the main parameter required by Solvency 2 (often called “Basel” for insurers). It involves calculating suitable forecast
values from the observed values as shown in the tables below, which shows monthly incremental cash collected.
LGD Modeling through Run off triangle
Origination
Vintage
Development Month
0 1 2 3 4 5
0 100 90 100 80 70 60
1 120 100 110 95 80
2 70 80 75 60
3 105 90 80
4 115 100
5 110
Please note that, all numbers reported in these
tables are only for illustration purpose
21

LGD Modeling
The problem is often stated using the cumulative recovery rather than the monthly recovery:
LGD Modeling: Run off triangle
Origination
Vintage
Development Month
0 1 2 3 4 5
0 100 190 290 370 440
50
0
1 120 220 330 425 505
2 70 150 225 285
3 105 195 275
4 115 215
5 110
The methodology called “Run-off Triangle/Chain-ladder” is used to predict recoveries because of its conceptual simplicity and its
practical implementation. Run off triangle approach is used to estimate the future cash recoveries for the defaulted book and then
will be discounted to default cohort/vintage to get the present value total recoveries.
The main underlying assumption for this method is that for every month since default (development month) the recoveries will show
similar trend for all cohorts. This means that a Development Factor, which is common to all the cohorts can be found for every
month since default.
One solution is given by the run-off triangle which represents the most recent development behavior, for each development month,
based on observed data.The following table illustrates how the coefficient is computed:
22

LGD Modeling
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The Development Factor is then used to extrapolate the future
cumulative cash recoveries of a given month from the last known
value:
23

LGD Modeling
We finally end up with the following matrix where the future cash recoveries are forecasted with the Chain-Ladder method:
LGD Modeling: Run off triangle
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The Development Factor (DF) will be used to estimate and extrapolate the future recoveries on the default book from the observed
recoveries.
It is necessary to discount all future recoveries to generate their present value. In order to reflect these requirements in the model,
the final recovery figures are to be discounted w.r.t. a discounting factor which will be estimated by analyzing the internal data or will
be provided by the bank.
24

LGD Modeling
Following steps highlights the process of estimating the LGD:
From the processed data the cumulative observed cash recoveries are computed
Compute the Development Factors
Forecast future Recoveries values for the unobserved development months
Compute the incremental recoveries from the above steps
Discount both the actual and forecast recoveries back to the date the account was transferred to default using the following formula:
( ) ( )
t
i
tAtDA
−






+⋅=
12
1
t corresponds to the development month, i.e. the number of months on default book for a given vintage
DA(t) is the Discounted Amount recovered in month t (discounted back to its date of transfer to the default)
A(t) is the amount recovered (either observed or forecasted) in month t, undiscounted and adjusted to the recovery expenses and legal cost
i is the annual discount rate
Where:
25
The LGD is then computed as :
( )
∑
∑∑
∈
∈ =
−=
Vl
l
Vl
M
k
l
OB
kDA
LGD 1
1
L corresponds to a given vintage
K corresponds to a given development month, i.e. the number of month on the default book for a given
vintage
DA (k) is the discounted amount recovered in month for the vintage
OB is the amount of the original default balances of a given vintage
V corresponds to the vintages that are selected to compute the LGD parameter
Where:

LGD Modeling: Downturn LGD
Under Basel, banks and other financial institutions are recommended to calculate 'Downturn LGD' which reflects the losses occurring during a
'Downturn' in a business cycle.
Following methods can be used to estimate Downturn LGD (DLGD):
Stress testing through macro-economic factors
PD-LGD modeling: LGD is defined as a lagged function of PD. It is based on the assumption that PD gets
impacted 12 -24 months before LGD
DLGD = function (max (current *PIT PD, highest PIT observed))
Highest value of observed LGD if portfolio has seen a full economic cycle and LGD is declining in the current
period
Fed Formula: DLGD = LGD*0.92 + 0.08
26

EAD Modeling Concept
Prediction Time Window = 12 Months
Observation
Point:
31 Aug 2016
Prediction Window End
Point: 1st
Sep 2017
Objective is to track
Balance for customers
who have moved from
Stage1 to Stage 3 in the
next 12 months
Here Stage1
(Performing)
Here in Stage 3
(90+ DPD)
For EAD modeling, customer Balance will be tacked for the next 12 months from the observation point, for the cases which are performing at the
observation point and have reached 90+ in next 12 months. Tracked balance at default will be estimated through EAD modeling
27

EAD Modeling
Utilisati
on
Balance
SEP 2016
(0-29
DPD) -
BAL
Balance
SEP 2017
(90+
DPD)
-EAD
Credit
Limit - CL
Credit
Conversion
Factor -
CCF
Estimated -
EAD
< 25% 2000000 2500000 4000000 0.35 2000
25% -
<50%
1500000 1700000 2000000 0.40 1750000
50 - <
85%
800000 900000 1000000 0.70 940000
>= 85% 900000 1030000 1000000 1.45 1045000
Total 5200000 6130000 8000000 6260000
Current Undrawn Limit (CUL)= CL – BAL
CCF = Function (EAD – BAL)
CUL
EAD Estimation = BAL + (CCF X CUL)
Instead of the Credit
Limit, Estimated EAD can
be used for the provision
calculation
Utilisatio
n
Balance
SEP 2016
(0-29 DPD)
- BAL
Balance
SEP 2017
(90+ DPD)
-EAD
EAD Factor Estimated -
EAD
< 1 Year 2000000 1500000 0.78 1560000
1- <2 Year 1500000 1250000 0.85 1275000
2- <3 Year 800000 750000 0.95 760000
>= 3 Year 900000 950000 1.08 972000
Total 5200000 4450000 4567000
EAD Factor =Function (EAD /BAL)
EAD Estimation = BAL * EAD Factor
Instead of the current balance, Estimated EAD can be
used for the provision calculation
Point to remember : EAD model should always over predict on the validation sample, usually 30% of the sample is kept outside for the validation
Credit Card Personal Loan
28

Modeling concepts
Basel vs. IFR S9
29

Basel for Business Managers- Retail Portfolio
It is a well prevalent idea in the banking that Basel is only a regulatory requirement for risk and not have any direct relation with the
following: Implication on the business decision process
Business decisions impacting the Basel outcome
Basel models (PD, LGD, EAD) can be used for the day to day business decisions. E.g. PD models can be used to make underwriting process
efficient, automated and smoother, automated risk based pricing and segmentation
Basel outcomes (risk adjusted return on capital –RAROC) can be combined with other measures (e.g. P& L performance) to decide the reward and
incentive estimates for various business units. E.g. for sales team incentive will be based on a matrix of the loan amount disbursed and risk rating
Basel assessment can enable Business managers to tap various business segments with right pricing which are untapped in the normal scenarios.
E.g. sub prime segments which are generally not targeted due to undefined risk of the segment
Basel models and the data stored for the calculations can be used for various business activities e.g. Collection, cross sell, balances & profit
reconciliation, provision management etc.
Basel models (PD, LGD, EAD) assessment and capital requirements are dependent on the business decisions related
Assessment of obligor’s ability and willingness to pay at the time of underwriting
Collection and recovery efforts on the delinquent customers
Quality, storage and accessibility of the data related to obligor at various life cycle stages over the period of time
Business decision regarding the lending book composition across various products e.g. Mortgage, Personal Loan, project finance etc.
will impact the Basel outcomes in various economic scenarios. In normal economic conditions, Mortgage heavy book will show less
Basel capital requirement whereas in the downturn same book will show more capital requirement.
Business decision regarding the lending book composition in various sectors e.g. mining, petroleum, services etc. will impact the Basel
outcomes in various economic scenarios
Business decision related to securitization, acquisition etc. will also impact Basel outcome 30

Basel with Bureau Data- Retail Portfolio
TraditionalApproach
Basel
Metrics –
PD, LGD,
EAD
Basel
Metrics –
PD, LGD,
EAD
Use only Bank’s data and arrange them as per the Basel Requirements
Customer performance across the market is not considered in the estimation
Basel estimates cannot be predicted for the portfolios with low number of records
No empirical estimates can be computed for the newly started portfolios
RecommendApproach
Basel
Metrics –
PD, LGD,
EAD
Basel
Metrics –
PD, LGD,
EAD
Combined Bureau information with Bank’s data to estimate Basel Metrics
Customer performance across the market is utilized in the estimation
Basel estimates with the help of the Bureau data can be predicted for the portfolios with low
number of records
Empirical estimates can be provided for the newly started portfolios through the peer level
market analysis
31

Basel combined with Bureau can Enable Fintech Lending Model: Retail Portfolio
Bureau Data
and Centre of
Excellence
Bureau Data
and Centre of
Excellence
Completely automated scorecards for PD, LGD
and EAD. These scorecards will have the
following special features:
Will tagged customer as “bad” based on their
performance across the market
Estimates will be more stable as they cover
more data than individual organizations
Empirical estimates will be available for the
portfolio with low number of defaults
Bureau score and other bureau information
based policy variables can be used for the
instant credit decision for Fintech lending,
examples are given below:
Utilization for cards across the market
Current balances as a ratio to the
original loan amount
Bureau Score Cut-off
Number and types of product in the
market
Length of credit history in the market
Delinquency behavior of the market
Based on the Bureau score and data,
Risk based loan amount and limit will be
assigned to the customer. It can be
completely automated and provides
better risk control for Fintech lending.
Bureau information can be converted
into predictive characteristics which in
turn can be used for to automate the
following activities other than
origination for Fintech Lending
Cross/Up Sell
Credit Limit Management
Collection Scorecards
Recovery scorecards
Regulatory capital and provision
requirements
32

Modeling concepts
Basel vs. IFR S9
33

IFRS9 Background
In July 2014, the international Accounting Standards Board (IASB) introduced the guidance on computing “Expected Losses”
for impairment accounting purposes know as “IFRS9” mainly to cater and solve the following:
Existing accounting standards are criticized to cover the losses actually incurred but do not caveat and provision the
expected losses on total book size – “ too little, too late”
IFRS9 computation is based on Expected Credit Loss(ECL) instead of incurred loss (IL)
All financial entities will mandatorily have to use IFRS9 modeling approach to compute provisions starting January, 2018.
IFRS9 divides customer life cycle into the following three
stages:
IFRS9 divides customer life cycle into the following three
stages:
Life
Cycle
Stage
Stage 1: Performing
Stage 2: Gone significantly Worse
Stage 3: Non-Performing (90+ DPD)
ECL
Component
Probability of Default (PD): probability of moving from one
stage to another (Stage1 to Stage2, Stage 2 to Stage3)
Loss given Default(LGD): Loss percentage on the default amount
for each stage
Exposure at Default (EAD) = Default balance amount at the time
of movement from one stage to another
IFRS9 requires three components to compute ECL :IFRS9 requires three components to compute ECL :
Stage 1 Stage 2 Stage 3
• Stage1 as 0-29 DPD
• PD needs to be computed to predict the
movement from Stage1 to Stage 3
• Life time LGD needs to be computed
• EAD needs to be predicted based on the
current balance and future default balance
Stage 1 to 2
PD1
LGD1
EAD1
Stage 2 to 3
PD2
LGD2
EAD2
Stage 3 till written off
PD3
LGD3
EAD3
• Stage2 as 30-89 DPD
• PD needs to be computed to predict the
movement from Stage2 to Stage 3
• EAD needs to be predicted based on the
current balance and future default balance
• Defined as 90 +DPD in IFRS9 guidelines
• PD =100% for this stage
covering Post write off recoveries etc.
• EAD will be equal to the outstanding
balance
34

IFRS9 comparison with IAS39
IFRS 9, Financial Instruments, is the IASB’s replacement of most of the guidance in IAS 39,Financial Instruments. Comparison between the
guild lines for the provisions calculations under IFRS9 and IAS 39 are provided below:
IFRS 9
Provisions
Incurred Losses
(DPD = 90+)
Expected Losses
(DPD < 90)
IAS 39
Provisions
Incurred Losses
(DPD = 90+)
Stage 1 DPD = 0 – 29 DPD
Stage 2 DPD = 0 -89 DPD
Stage 3 DPD = 90 + DPD
Stage-wise definition is based on the best industry practices
IAS 39 covers only incurred losses for the provisioning, therefore, Provisions are not provided for Stage1 and Stage2
35

Basel and IFRS9Differences
Basel
IFRS9
Regulatory DirectionRegulatory Direction
Minimum Capital
Requirement
Estimation
Loss Provision
Requirement
Estimation
ConceptConcept
Deals with Expected
and Unexpected
losses
Deals only with
Expected losses
Default StagesDefault Stages
Simplified approach:
Default (90+ DPD)and Non
Default (0-89 DPD)
Multiple level approach:
Stage1 (0-29 DPD)
Stage 2(30-89 DPD) Stage3
(90+DPD)
Prediction WindowPrediction Window
Prediction Window:
•PD: 12 months
•LGD: Life time
•EAD: 12 months
Prediction Window:
•PD: 12 months/Life time
•LGD: Life time
•EAD: 12 months/Life time
Similarities
Basel
IFRS9
Calculation
Components
Calculation
Components
Requires PD, LGD
and EAD Metrics
ConceptConcept MethodologyMethodology
ECL(Expected losses)
=PD X LGD X EAD
is essential for both
Basel and IFRS9
Methodologies for both Basel and IFRS9 requires PD, LGD and EAD
estimates. If the organization has already developed Basel models
then the outcome of the same models with some adjustments can be
used for the IFRS9 ECL computation
36

Basel and IFRS9
Point in Time (PIT) PDPoint in Time (PIT) PD
Through the Cycle (TTC) PDThrough the Cycle (TTC) PD
Life time PDLife time PD
Lifetime LGDLifetime LGD
Downturn LGDDownturn LGD
12 month EAD12 month EAD
Life Time EADLife Time EAD
P & L ImpactP & L Impact
Capital ImpactCapital Impact
Heat maps can be used to compare the portfolio quality as per Basel and IFRS 9 in the following way
PD
LGD
High
Medium
Low
HighMediumLow
Note: Size of the circle represents
the total balances in that category
Avg PD = 3%
Avg LGD =
80%
Avg PD =
5.5%
Avg LGD =
90%
Avg PD =
3.5%
Avg LGD =
83%
Avg PD =1%
Avg LGD =
65%
PD
LGD
High
Medium
Low
HighMediumLow
Avg PD =
3.5%
Avg LGD =
80%
Avg PD =
5.5%
Avg LGD =
90%
Avg PD =
6.5%
Avg LGD =
80%
Avg PD =0.5%
Avg LGD = 60%
Illustration
Purpose Only
Basel IFRS9
37

Major Changes from Basel 2 to Basel 3
Capital Conservation Buffer: Another key feature of Basel III is that now banks will be required to hold a capital conservation
buffer of 2.5%. The aim of asking to build conservation buffer is to ensure that banks maintain a cushion of capital that can be
used to absorb losses during periods of financial and economic stress
Countercyclical Buffer: This is also one of the key elements of Basel III. The countercyclical buffer has been introduced with
the objective to increase capital requirements in good times and decrease the same in bad times
Minimum Common Equity and Tier 1 Capital Requirements:
The minimum requirement for common equity, the highest form of loss-absorbing capital, has been raised
under Basel III from 2% to 4.5% of total risk-weighted assets
The overall Tier 1 capital requirement, consisting of not only common equity but also other qualifying
financial instruments, will also increase from the current minimum of 4% to 6%.
Although the minimum total capital requirement will remain at the current 8% level like Basel 2, yet the
required total capital will increase to 10.5% when combined with the conservation buffer
The basic structure of Basel II remains unchanged with three mutually reinforcing pillars in Basel III. The major changes from Basel II are the
following:
Requirements Under Basel 2 Under Basel 3
Minimum Ratio of Total Capital to RWAs 8.00% 8.00%
Minimum Ratio of Common Equity to RWAs 2.00% 4.50%
Tier1 Capital to RWAs 4.00% 6.00%
Core Tier 1 Capital to RWAs 2.00% 5.00%
Capital Conversion Buffers to RWAs None 2.50%
Leverage Ratio None 3.00%
Countercyclical Buffer None 0% to 2.50%
Liquidity Coverage Ratio None 100%
Leverage Ratio None 3.00%
38

About the Authors
Ganesh Viswamani is Head of Retail Finance at Finance House with more than 25 years of experience in the financial services industry, he has
headed various business and analytics units at global organizations in his career for multiple geographies. He is a specialist in startup and green
field projects and has successfully transformed businesses with the help of technology and analytics advancement across the credit life cycle
for retail & SME products.
He can be reached at ganesh.viswamani@fh.ae
Prakash Bhatt is Head of Analytics at Finance House with more than 11 years of experience in Risk & CRM analytics with special focus on Big
data based Bureau solutions, IFRS9 modeling and implementation, Basel & Impairment under AIRB (PD, LGD and EAD models), Attrition
management, Campaign management, CL management, Fraud management, Line management, Scorecards for retail and corporate products.
He can be reached at prakash.bhatt@fh.ae
About Finance House Abu Dhabi
Finance House PJSC (FH) founded in 2004 is a leading financial institution operating in the UAE. FH offers an array of financial products and
instruments for retail and corporate sector, FH is regulated by the Central Bank of the United Arab Emirates and listed on the Abu Dhabi
Securities Exchange. FH is a pioneer and market leader in using internal and bureau data along with advance analytics and decision systems for
faster & accurate decision process, higher customer satisfaction, enhanced profitability and better risk management.
39

Basel from a Business Perspective

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