MAT012 Credit Risk Scoring
Assignment 2020/21
This forms your assessment (100%) of this module.
There are two parts to this assessment.
Part A contains THREE short essay-based questions and counts for 50% of the final mark.
Each essay should be around 1,000-1,500 words in length.
Part B contains FIVE 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.
You must answer ALL questions.
PART A
1. Critically examine what needs to be considered when developing a credit risk scoring
model.
[20 marks]
2. Discuss how survival analysis and multi-state (Markov chain) models may be used in
credit risk modelling and the challenges they present.
[15 marks]
3. Detail the history of the Basel Accords and discuss the challenges in modelling the
credit risk on a portfolio of consumer loans.
[15 marks]
PART B
1. Split the dataset into two subsets as follows:
Subset 1: the applicants with Checking = 1 or Checking = 2
Subset 2: the applicants where Checking = 3 or Checking = 4
Clean the subsets if necessary.
[5 marks]
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.
[5 marks]
3. For each training set choose four variables which are suitable for building a scor
ecard. 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.
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]
4. Using the binary variables obtained from the coarse classification in the above exercise to
build two scorecards for each training set, one using linear regression, the other using
logistic regression. Note this means you should have four scorecards in total:
(i) using linear regression for Checking = 1 or 2;
(ii) using logistic regression for Checking = 1 or 2;
(iii) using linear regression for Checking = 3 or 4;
(iv) using logistic regression for Checking = 3 or 4;
Note that the file you submit should include, in the Appendix, a table that gives the binary
variables you used, together with the coefficientsfor those variables calculated in each
regression.
[15 marks]
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]