credit scoring model
TRANSCRIPT
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Credit scoring modelBy
Batchu Satish
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Credit scoring can be formally defined as a statistical
method that is used to predict the probability that a loan
applicant or existing borrower will default or become
delinquent.
It is a systematic method for evaluating credit risk that
provides a consistent analysis of the factors that have been
determined to cause or affect the level of risk.
The objective of credit scoring is to help credit providers
quantify and manage the financial risk involved in
providing credit so that they can make better lending
decisions quickly and more objectively.
What is credit scoring?
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Credit scoring was primarily dedicated to assessing
individuals who were granted loans, both existing and new
customers.
Credit analysts, based on predetermined scores, reviewed
customers! credit history and creditworthiness to minimi"e
the probability of delinquency and default.
Credit scoring process includes collecting, analy"ing and
classifying different credit elements or variable to assess
the credit decision.
#ome times credit score will help us to identify the
corporate bankruptcy.
Credit scoring
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$ayment %istory
&aking late payments, defaulting '&I s or dues shows trouble and
will give negative affect to the score
%igh utili"ation of credit limits
Increase current balance of credit limit is affect the credit scoreadverse.
%igher percentage of unsecured loans
instead have unsecured loans, having both secured and unsecured
loans will show positive affect for credit score. &any new accounts opened recently
%aving multiple loans and credit cards will increase the debt burden and it
negatively impact on credit score.
Variables included to Build creditscore(CIBIL)
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35
30
25
10
payment history Credit limit
Types of loans new credit
Proportion of variable in creditscoring
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$erformance chart to Identify cutoff
score
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( standard life cycle model for credit scoring designed on
the basis of (I)* approach+*(#' II capital requirement-
ife cycle of any model is defined three phases i.e.
assessment, implementation and validation. Model assessment
◦ In order to develop credit scoring model we need past
behavior of client data, so that we can assess the
$robability of default or nondefault.◦ If past details of client and sufficient data is available we
go for empirical model, it is used for existing clients.
◦ If past data is not available, new client, an expert or
generic model is suitable for solution.
Credit scoring odels life cycle
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It allows the banks to implement automated decision systemto manage their retail client
In implemented process the main task for credit manager is
to define most appropriate and efficient threshold cutoff to
credit model To maximi"e the benefit of scoring model, cutoff should be
set taking into account of all misclassification accounts of
TypeI and TypeII errors.
odel I!ple!entation
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*ack testing and benchmarking are two important aspects in
scoring model validation.
/ith the back testing credit analyst identify the calibration
and discrimination of scoring model.
*enchmarking is another quantitative validation method
which aims at assessing the consistency of the estimated
scoring models with those obtained using other estimation
techniques, and potentially using other data sources.
This analysis maybe quite difficult to perform for retail
portfolios given the lack of generic benchmarks in the
market
odel validation
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ethodology "evelop!entphase
#e!ar$s
DiscriminantAnalysis*
1940-1941 0ormality restriction
o!istic re!ressionand pro"it analysis*
19#1 onwards
It mostly deals with
categorical qualitativevalue thus It does not
require 0ormalityassumption
Decision tree andCA$T**
%rom 1994The accuracy of above twomodels was not very high.
/ith high implementationof machine these modelsare exhibited as well as
accuracy was high
&e'ral &etwor(s** %rom 1995
)eneticpro!rammin!**
%rom 1994
"evelop!ent of credit scoring!odels over a period of ti!e
123 Traditional or conventional statistical method
1223 (dvanced computeri"ed methodologies
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%or ndi+id'al,ith 'se of some methods we
can assess the c't-o. as well as comp-
re with di.erent methods and then
identify the "est c't-o./
Conceptual fra!e%or$ for creditscoring
ndi+id'al orrowers
aria"le consider forcredit scorin!A!e
Time at presentaddress
rofessionri+atep'"lic sector
Time at c'rrentprofession
onthly re+en'eso'se owner
&o pre+io's creditsD'ration of the loan
Amo'nt and type ofloan
And otherdemo!raphic
C't-o. Appro+alof loan
ncl'de someparameters whichcan impro+e the
score
67
8
&o
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%or corporate clients, This method is 'sef'l to identify the "an(r'ptcy
e+el of corporate client87s:/
Conceptual fra!e%or$ for credit scoring
Corporate clients
dentify the $atios whichtells a"o't ;nancialposition of client
i-score
$e?ect the application
a(edecision
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It is very important to determine the sample si"e before the
model build
The more sample consideration, the more accuracy can
expect.
4or individuals5 it is more important to incorporate the
variable like behavioral, economic to estimation of
probability of default+$6-.
4or corporate+#&'s-5 financial ration will help to predictthe financial and asset position of the client.
Sa!ple si&e and !ethodology toBuild a credit scoring
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4or individuals5
◦ credit score be one of the most important factors in
determining whether or not you are approved for credit.
◦
it also be a major factor in determining the terms andconditions of the loancredit extension.
◦ It can help to understand the interest rate structure for loan
applicant.
low credit score7 high interest +viceversa-
• 4or corporate clients8
It useful to determine the bankruptcy position of firm.
• 4or *anks and financial institutions8
It is very helpful to predict the customer default position with cutoffscore, also 1good! or 1*ad 9customer.
#ationale for credit score
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:ver a period of time many credit scoring models are exhibited
to identify the default nondefault position for customer.
$aradigm shift has taken in credit scoring models from
Traditional statistical to modern methods but these method
couldn!t incorporate the important situations such as behavioral,errors of credit scoring and macro economic conditions.
It is the big gap for both financial institutions as well as
individuals.
(lso some technical issues will be make customers as default. /hen creating and building a credit scoring models
incorporating those variable which have been left over the period
of time will give better fit model. This will improve the
profitability and decrease the customer default position
%rap'up re!ar$s