credit scoring model

8/19/2019 Credit Scoring Model

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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

credit scoring model

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