# AnalyticBridge

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# Credit risk modeling doubts

Hi.. i'm new in analytic field & trying to learn building Credit risk modeling on my own & facing few difficulties(As I don't know how risk modeling actually gets implemented in Banking  system) After reading few thesis papers & information available on internet i developed some understanding of credit risk modeling(PD) & thus prepared steps that needs to be taken while developing risk modeling which is as below:(Logistic Regression)

Bankruptcy process already started or payment due for more than 90 days(basel-2)

2. Sampling

• Sample size needs to be decided to build the model.
• General Formula used={ (Z(alpha/2)sqrt(PDmax(1-PDmax))/Del PD}^2

PD=Max prob of default estimated by experts through analyzing previous years data

Del PD= PD error

• Logistic Regression recommends to use 80% of Good & 20% of Bad thus accordingly we need to modify our data.i.e deleting the goods & increasing the bads.

Data Manipulation

• Missing value treatment
• Outlier detection mainly univariate analysis
• Outliers treatment

-replace all variable having z score > 3 by mean + 3* S.D(Proc Standard)

- replace all variable having z score <- 3 by mean - 3* S.D

-How to decide number of group (Expert judgement?) & how grouping can be done

3. Analysis of Variables choosing statistical model form & calculation of coefficients

• Variables can be chosen through

A. Expert judgements

B.Economic logic, discriminatory power of variables (& other criteria needs to be considered

• Input Selection

-       Cramer’s V,chi sq

-       P-value

• Grouping of attribute values

-       Based on percentile/Attributes (I’m confused with SAS code for the same though I know pctlpts can be used for calculating percentile but do we need to specify each & every variables...Really confused with this...in some paper it was mentioned that grouping should be done based upon WOE...

-       Code the values-apply weight of evidence(WOE)

-       Calculate Information Value

C. Applying forward selection (Mostly used)

4.Validation of Model

-       ROC

-       Ginni coefficient

-       Lorenz curve

-       KS distance

5.Implementing score Card Scaling

-       Score=offset  + factor*ln(odds)Score + PDO(Points to double the odd)

-       Factor=PDO/ln(2)

-       Offset=Score(factor*ln(Odds))

6.Reject Inference

-       Augmentation

-       Extrapolation

If possible please help me out as i don't have any contacts in Analytic field who can guide me..or please suggest me some really good stuff so that i learn read & learn on my own..

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### Replies to This Discussion

Here are a couple of books that give a good overview of consumer credit risk modeling (with emphasis on scorecards.) They are both fairly quick reads, and should be fairly inexpensive.

Credit Scoring for Risk Managers, The Handbook for Lenders - Elizabeth Mays

Credit Risk Scorecards - Naeem Siddiqi

These are not highly technical, so you would have to look elsewhere for advanced model diagnostics, etc. These do not focus on the Basel (PD, LGD, EAD) framework, and they are not really applicable to corporate/government credit risk.

Hope they help...

hey Thanks....

yesterday only i got Naeem Siddiqi..will get back to you once i finished reading it...:)

Thanks & Regards

Amitesh

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