UNIVERSITY*OF*CARDIFF*
* MAT012*Credit*Risk*Scoring* * Assignment*2019/20*
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This!forms!your!assessment!(100%)!of!this!module.!
There!are!two!parts!to!this!assessment.!
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Part*A!contains!THREE!short!essay>based!questions!and!counts!for!50%!of!the!final!mark.!
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Part*B!contains!FOUR!tasks!to!establish!a!scorecard!using!the!given!dataset!and!counts!for! 50%!of!the!final!mark.!You!may!use!Excel,!SAS,!R!or!Python!to!assist!in!the!scorecard! preparation.!
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You!must!answer!ALL!questions.!
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Submission!must!be!made!by!3pm*on*Friday*20th*March!via!Learning!Central,!and! instructions!will!follow!shortly!on!how!to!do!this.!You!will!need!to!submit!a!single!file! containing!answers!to!all!questions;!any!spreadsheet!analysis,!workings!or!coding!necessary! can!be!shown!in!an!Appendix!in!that!file.!Only!the!submitted!file!will!be!marked.!
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* PART*A*
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model.! !
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1.! Critically!examine!what!needs!to!be!considered!when!developing!a!credit!risk!scoring!
2.! Explain!how,!in!theory,!Cox¡¯s!proportional!hazard!model!for!survival!analysis!can!be! used!for!constructing!a!scorecard.!Comment!on!the!relative!popularity!of!Cox¡¯s!PH!
model!versus!logistic!regression!in!scorecard!construction.!! !
[15!marks]! 3.! Provide!a!brief!literature!review!on!the!use!of!Markov!models!in!credit!risk!
modelling,!with!a!particular!focus!on!those!used!in!credit!risk!scoring.!
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[15!marks]!
[20!marks]!
PART*B*
! The!dataset!underpinning!the!analysis!here!is!that!used!in!the!lab!sessions!during!lectures.!It! has!been!uploaded!as!a!spreadsheet!named!¡®German¡¯!together!with!the!data!dictionary! ¡®German!data!dictionary¡¯!describing!each!attribute.!You!will!recall!that!the!dataset!consists! of!data!for!1000!applicants!along!with!a!variable!that!says!whether!they!were!subsequently! Good!or!Bad!from!a!credit!perspective.!
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1.! Split!the!dataset!into!two!subsets!as!follows:!
Subset!1:!the!applicants!with!Duration!<=!12!months! Subset!2:!the!applicants!where!Duration!>!12!months!
Clean!the!subsets!if!necessary.!
[5!marks]!
[5!marks]!
scorecard.!For!each!training!set!the!variables!must!have!(i)!at!least!one!continuous! variable!before!binning;!(ii)!at!least!one!categorical!variable!with!more!than!two! categories,!so!you!can!see!whether!categories!can!be!combined.!!
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Explain!the!rationale!behind!your!choice!of!variables!(using!supporting!statistics!eg! chi>square).!Should!you!be!unable!to!choose!variables!satisfying!the!above!criteria,! explain!the!problem!you!have!encountered!and!the!solution!you!have!chosen!to! compromise!the!variable!selection.!
[10!marks]!
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4.! Using!the!binary!variables!obtained!from!the!coarse!classification!in!the!above!
exercise!to!build!two!scorecards!for!each!training!set!(so,!two!scorecards!for!those! applicants!with!Duration!<=!12!months;!another!two!for!those!with!Duration!>!12! months),!one!using!linear!regression!and!one!using!logistic!regression.!!
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2.! For!each!subset,!establish!a!training!set!and!validation!set.!Explain:!
a.! what!principle!you!have!used!to!decide!on!these;!
b.! why!both!training!and!validation!sets!are!needed;! c.! any!issues!encountered!during!the!splitting!exercise.!
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3.! For!each!training!set!choose!four!variables!which!are!suitable!for!building!a!
Note%that%the%file%you%submit%should%include,%in%the%Appendix,%a%table%that%gives%the% binary%variables%you%used,%together%with%the%coefficients%for%those%variables%
calculated%in%each%regression.%
[15!marks]!
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5.! Derive!ROC!curves!for!all!scorecards!using!the!validation!set!applicable!to!each,!
showing!in!detail!how!sensitivity!and!specificity!have!been!calculated.!Estimate!the! Gini!coefficient!and!KS!values!for!each.!Explain!and!comment!on!your!results.!
[15!marks]!