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Predictive Model for Loan Approval Process using SAS 9.3_M1

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This is a Predictive Model which uses Logistic Regression to statistically help make better loan approval decisions in future for a German Bank. It uses an historical credit data set with 1000 data points and 20 variables.

Tool used:
SAS 9.3_M1

Steps Involved are:
- Data Quality check using Correlations and VIF Tests
- Analysis of different Variable Selection Methods such as Forward, Backward and Stepwise
- Variable Selection on the basis of Parameter Estimates and Odds Ratio
- Outlier Analysis to identify the outliers and improve the model
- Final Model Selection Decision based on ROC curve, Percent Concordant, PROC Rank and Hosmer Lemeshow Test

Published in: Marketing

Predictive Model for Loan Approval Process using SAS 9.3_M1

  1. 1. German  Bank   Loan  Approval  Decision  Model   Mohamed  Ibrahim                                                    Rahul  Goel     Akanksha  Jain                                                                      Chhavi  Sharma      
  2. 2. "Houston,  We've  Had  a  Problem"   On  average,  one  of  every  nine  loans  you  grant  is  defaulted  on   2  
  3. 3. Objec<ve  &  Background   Objec<ve:     Use  historical  credit  dataset  to  develop  a  predicAve  model  to  make  beBer  loan   approval  decisions  in  the  future       Background:   On  an  average,  ~11%  loans  granted  are  defaulted  in  the  current  scenario.       Scope:     German  Credit  Dataset   –  1000  entries  &  20  variables   –  Historical  pre-­‐loan  data     –  Dependent  variable:  Good_Bad  (Binary)   Steps:   –  Data  Quality  Check   –  Variable  exploraAon  and  transformaAon   –  Method  SelecAon(forward,  backward,  stepwise)   –  Removal  of  outliers   –  Run  the  LogisAc  Regression  Model     3  
  4. 4. Variable  Transforma<on   4  
  5. 5. Variable  Transforma<on(cont..)   5  
  6. 6. Data   Variables   Data  Drill  Down   6  
  7. 7.  Variable  Selec<on   1 2 3 4   Full  Model  using   STEPWISE  variable   selec<on  method     Full  Model  using   BACKWARD  variable   selec<on  method   Full  model  using   FORWARD  variable   selec<on  method:   Model  all  variables   from  STEPWISE  +   variable  age_new     Variables:     checking_new   duraAon_new   history_new   purpose_new   savings_new  installp   marital  coapp    other   foreign_new     Variables:     checking_new   duraAon_new   history_new   purpose_new   savings_new  installp   marital  coapp  other   foreign_new       Variables:   checking_new   duraAon_new   history_new   purpose_new   savings_new  installp   marital  coapp    other   foreign_new   amount_new   age_new         Percent  Concordant   82.3   c  0.823       Hosmer  –  Lemeshow   Test  :   0.9398       SL  Entry:  0.1         Variables:   checking_new   duraAon_new   history_new   purpose_new   savings_new  installp     marital  coapp   age_new  other   foreign_new     age_new       Percent  Concordant   82.0   c  0.820       Hosmer  –  Lemeshow   Test  :   0.7475     Variables:   checking_new   duraAon_new   history_new   purpose_new   savings_new   installp  marital   coapp  age_new   other  foreign_new   age_new     Percent  Concordant   84.1   c  0.841       Hosmer  –   Lemeshow  Test  :   0.6367     SL  Entry:  0.1     SL  Stay:    0.05   SL  Entry:  0.1     SL  Stay:    0.05   Percent  Concordant   81.8   c  0.818   Hosmer  –  Lemeshow   Test  :   0.7535   SL  Entry:  0.1     SL  Stay:    0.05     Percent  Concordant   81.8   c  0.818       Hosmer  –  Lemeshow   Test  :   0.7535       SL  Stay:    0.05     5 Stepwise  with   age_new  aLer   removing  outliers   7  
  8. 8. Final  Model   8  
  9. 9. 42.3%  defaulters  were  classified  as  “bad”  at  55%  or  more   9  
  10. 10. Parameter  Es<mates   10  
  11. 11. Parameter  Es<mates  (cont..)   11  
  12. 12. Who  “not”  to  target     The  odds  raAo  &  parameter  esAmates  suggest  that  the  business   should  avoid  targeAng  individuals  with  the  following  characterisAcs   (not  arranged  in  order  of  priority)     –  –  –  –  –  –  –  Have  lower  balances  in  checking    &  savings  account   Have  a  delinquent  credit  history     Do  not  have  a  guarantor     Have  other  installment  plans   A  non  resident   Lower  age   High  debt-­‐to-­‐income  raAo   **  The  list  of  characterisAcs  suggested  by  the  model  is  for  a  given  set  of  1000  data  points  and   should  not  be  extrapolated  to  other  scenarios.  This  interpretaAon  is  only  for  the  purpose  of  a   classroom  project  and  should  not  be  used  otherwise.   12  
  13. 13. Thank  You   13  
  14. 14. Appendix   •  •  •  •  CorrelaAon  Results   Proc  Means  &  VIF  Test   Outliers   Proc  Rank   14  
  15. 15. Correla<on  Results   15  
  16. 16. Means  &  VIF   Variance  Infla<on  Factor   Proc  Means   16  
  17. 17. List  of  16  Outliers  in  the  Dataset   17  
  18. 18. Rank  Results   18  

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