1

The Credit Risk

Evaluation Model

By Maria A Canaveras Galdon, Jasdeep Kaur, Ashwani Dua, Ankita Jiwrajka, Shruti Choudhary, Mihai Enescu

2What is Credit Risk?

The risk of loss of principal or loss of a financial reward stemming from a borrower's failure to repay a loan or otherwise meet a contractual obligation.

Credit risk arises whenever a borrower is expecting to use future cash flows to pay a current debt. Investors are compensated for assuming credit risk by way of interest payments from the borrower or issuer of a debt obligation.

The higher the perceived credit risk, the higher the rate of interest that investors will demand for lending their capital. Credit risks are calculated based on the borrowers' overall ability to repay.

3Data UsedVariable Name Description TypeSeriousDlqin2yrs Person experienced 90 days past due delinquency or

worse Y/N

RevolvingUtilizationOfUnsecuredLines

Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits

percentage

age Age of borrower in years integerNumberOfTime30-59DaysPastDueNotWorse

Number of times borrower has been 30-59 days past due but no worse in the last 2 years.

integer

DebtRatio Monthly debt payments, alimony,living costs divided by monthy gross income

percentage

MonthlyIncome Monthly income realNumberOfOpenCreditLinesAndLoans

Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)

integer

NumberOfTimes90DaysLate Number of times borrower has been 90 days or more past due.

integer

NumberRealEstateLoansOrLines Number of mortgage and real estate loans including home equity lines of credit

integer

NumberOfTime60-89DaysPastDueNotWorse

Number of times borrower has been 60-89 days past due but no worse in the last 2 years.

integer

NumberOfDependents Number of dependents in family excluding themselves (spouse, children etc.)

integer

4Trained Data

Test Data

Apply Discriminant Function Analysis

Apply Neural Networks

Apply Neural Networks

Apply Discriminant Function Analysis

Apply Logistic Regression Model

Risk rate of each Candidate

Maximize Return of Organization and find loan amounts

5

Whom to deny the loan after preliminary screening?

6

Artificial Neural network

7What is ANN? A neuron is a special

biological cell that processes information.

Receives signal from one neuron.

Transmits information to another neuron.

ANN is inspired from biological neurons.

Consists of extremely large number of processors with many interconnections.

Uses:- Function Approximation- Pattern Classification- Prediction/Forecasting

8Architecture Inputs represented by “i”.

Inputs connected to neuron using weights(w)

Output at neuron:function using SumProduct

(inputs,weights)

ACTIVATES the neurons.

Decided based on output.

In our model we need an output of 0 or 1.

Function =>Logistic Sigmoidal

Where, x = w1i1 + w2i2 + w3i3

Bias: y = mx + c

Activation Function

9Implementation in Excel - Solver Set of inputs interact to produce the

output.

The output can be 0 [ Not Default] or 1 [Default].

7 input variables and single output variable.

ANN used to predict the output [0,1].

Steps

Create an ANN- 7 inputs, 6 hidden neuron layer and 1 output.

Select the Activation function.

Optimize for the weights using Solver

Minimize the error.

Error Function

E= [Expected – Actual]2

Minimize the sum of these errors.

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ANN - Solver Demo

ANN_test_modelANN_Training_model

11Discriminant AnalysisDiscriminant analysis is a

statistical tool used to clustered together members of a population because of similar

characteristics. the clusters (usually called groups) are

predefined

Steps

Create Score function = w1*p1+w2*p2+…+wn*pn

Get results of Score function for all the samples

Compare results of score function with a cutoff value. If sample score function result is above cutoff, samples will be classified in group one, otherwise group two

Decision variables will be weights and cutoff value.

Objective Function

Objective Function = Correct Classified/Total Samples

Maximize percentage of Correct Samples Classified. Evolutionary Solver used

Implementation in Solver

12Discriminant Analysis Sensitivity Analysis

Biased Data

Algorithm Improvement Possibility - Really good Performance, however possibility to improve detection of positive cases

Actual/Expected

1 0

1 3 0

0 185 2812

Steps

Modify model adding a Constraint for % of positive correctly classified.

Still objective function still is to maximize Total % of correct classifications

Run solver table to see maximum total classified when varying the % of positive correctly classified

0.69

9999

9880

7907

1

0.71

9999

9690

0558

5

0.74

0000

0095

3674

3

0.75

9999

9904

6325

7

0.77

9999

9713

8977

1

0.80

0000

0119

2092

9

0.81

9999

9928

4744

3

0.83

9999

9737

7395

6

0.86

0000

0143

0511

5

0.87

9999

9952

3162

8

0.89

9999

9761

5814

20.000.400.80

Sensitivity Positive vs. Total Correctly Classified

% Positive Correctly Classified

% Total Correctly

Classi-fied

Class Prob

1 93.3%

0 6.6%

Implementation in Solver Table

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Discriminant Analysis - Solver Demo

Microsoft Excel Worksheet

1414

TRAINING TEST

% Correct % Correct

93.3% 93.83

TRAINING TEST

% Correct % Correct

94.3 90%

Actual/Expected 1 0

1 10 106

0 194 2690

Actual/Expected 1 0

1 3 0

0 185 2812

Discriminant Analysis Neural NetworksV/S

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What credit interest to charge??

How much loan to approve??

16Logistic Regression

Logistic Regression is a classification technique

The dependent variable which is in binary for (0 and 1)

Independent variables can be continuous or categorical

The logistic function provides logit Log (odds of event

happening)

The logit is used to calculate the probability of an event happening

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Dependent Variable

Person denied the loan (Y/N?)

Independent Variable

Age

Debt Ratio

Revolving Utilization Of Unsecured Loans

Number Real Estate Loans Or Lines

Number Of Open Credit Lines And Loans

Number Of Time loan due more than 30_59 DaysNumber Of Time loan due more than 60_89 DaysNumber Of Times loan due for more than 90 Days

Logistic Regression- Our Model

18Methodology

Combined DATA from Discriminant Analysis

0: Loan Accepted1: Loan Denied

Test Trained

Run Logistic Regression

Using SAS

Logit value varied with income Divided applicants into income quartile and

ran separate logistic regression Record logit value for each variable and

each income band

Step I Step II

Probability of loan rejected Probability of Default

19Classification into Credit RatingProbability Non-

DefaultProbability

DefaultCredit

RatingsInterest Rates

84% 15.5% D 10.00%

91% 9.1% D+ 9.75%

94% 5.7% C 9.50%

96% 4.1% C+ 9.25%

97% 3.2% B 9.00%

97% 2.6% B+ 8.75%

98% 2.2% A- 8.50%

98% 1.8% A 8.25%

99% 1.5% A+ 8.00%

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Credit Risk and Return Model Demo

Credit Risk and Return Model

21How much loan should be accepted?

Objective

Constraints

Decision Variable

Maximize total return= ∑(interest earned on loans approved)

Amount of loan accepted for each candidate

• Loan Approved < Loan Applied (For each candidate)• Loan Approved < Annual Income (For each

candidate)• Risk Taken*** < Maximum risk rate defined by

management• ∑ (Loan Approved ) < Funds Available to distribute

***Risk taken= ∑ (Probability of default X Loan Approved ) ∑ (Loan Approved )

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Do I need any additional variables in calculating the credit rating ??

How can respond to instant line of credit increase??

Can I use the same model in new emerging countries ??

23Accommodate Additional Variable

Current Model is based on the numerical variables

Bank’s loan form collects Numerical variables Categorical variables Comments providing additional

information about the variables

Categorical Variable

Analyze Additional Variables

Assign Codes to Categorical Variables. Own Home – 1, Rented - 0.5

Are there any numerical variable that need code assignment - Number of Dependents ?

Include the Code assigned Categorical Variables in the current models

Comments / Explanation

How many potential customers have provided comments? Are codes generic or specific to a variable ?

Is any Public Information involved?

Run Text Analyzer on the comments

Assign Codes based on the historic data and include them in current model

Additional Steps

Prepare the Data

Review Codes regularly

Explore use of web services to access data from public info sites.

Benefits

Realistic, Dynamic automated Credit rating

Reduction in Cycle Time & Disputes !

Optimum Portfolio Selection !!

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Approve Disapprove V/S

Real time Credit Line increase Approval International Credit Line Request due to medical charges

Current Model is based on the data points for bank loan application

Accommodate credit card application data point

Run suggested models with the credit card information for Training and Test

Calculate Bias Decide on the additional Utility Factor for

credit card based on Bias Model is available to use.

Steps

Bank receives a request

Run past payment history through the model

Has customer updated bank on travel plans. No -Analyze Travel History from the model – location of past payments. Is he a frequent traveler .

Analyses type of request – Medical or Luxury ??

Run Logit Regression to Approve or Disapprove

Benefits

Dynamic, Instant credit line approval to increase the utility function for the credit card

25Use Model to Approve Loans in Emerging Market

Application of the Model

Is Data Available for Baseline?

Based on the financial history of the country , readjust the variables, weightage , codes .

Run Training Test and Training sets . Calculate Bias.

Confirm Clusters for Discriminant Analysis, Calculate Cut off and confirm on the eligibility?

Run Logit Risk and return Model to calculate the probability of Default ranges

Benefits

Comparative Analysis with home Country to design new products

Shorter Cycle Time

Reduction in Rate with Automation of the calculation and Batch processing.

Increase in Regulation

Intensifying competition

Increasing costs

Source: http://www.ey.com/Publication/vwLUAssets/EY_-_Banking_in_emerging_markets:_Investing_for_success/$FILE/EY-Banking-in-emerging-markets-Investing-for-success.pdf

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Q & A

THANKS !!