MATH60131/70131 CONSUMER CREDIT RISK MODELLING - PROJECT 2022
In this exercise you will be assigned a data set of personal loans and you will
build 2 scorecards using the techniques taught on the Consumer Credit Risk
Modelling course. You will use the R statistical language for your work and
report the methods you use and your results in a written report.
Total contribution to assessment: 25%.
DEADLINE
Your coursework must be submitted by 1pm on Monday 5 December 2022.
INSTRUCTIONS
1. On Blackboard Learn, in the Coursework folder, you will see ten data
files of the form LCdata_n.RData. For this coursework, download and use
the file where n = the last digit of your College ID. Load this file into your
R using the load function; eg
load(“H:\\data\\LCdata_1.Rdata”)
will load data from folder data into a data frame named D1. The data is
described in Appendix A below.
2. First, you will build a logistic regression model of non-default, where default
is given by the def_flag variable and with only the predictor variables:
loan_amnt, grade, emp_length_p, term and addr_state. Analyse each of
these variables and decide which transformation or processing is required to
include them in your model, if any.
3. Split the data randomly into a training data set and a test data set, with the
ratio 2:1 of observations in each.
4. Build a scorecard using a single logistic regression model with the outcome
and predictor variables as described and processed in step 2.
 Do not include interaction terms.
 Build the model using only the training data set.
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5. Interpret your scorecard, considering which predictor variables are
important in your model and how they are associated with creditworthiness,
in terms of direction and statistical significance of association. Use a
significance level of 0.01.
6. Use your scorecard to construct the ROC curve and compute the area under
the ROC curve (AUC) for both the training and test data set. What does the
difference in AUC between training and test tell you about your model, if
anything?
7. Secondly, you will apply some of the techniques you have learned so far on
the course to improve the model you built in step 4. You can make use of
any of the variables supplied in the data set. Use AUC as your performance
measure to determine which model is best.
When doing this work, you should consider methods dealing with:
a. Data preparation and validation.
b. Variable selection.
c. Model structure. Consider including interaction terms or building a
segmented model.
d. Testing. How will you use your data to ensure testing is performed
correctly?
8. Write a report describing the methods you tried and the results you
achieved. Report the model built in step 4 and the final model you tried that
performed best and give an interpretation of it, especially in contrast to the
model you built in step 4.
9. Notes:
a. Your report should be between 5 and 10 pages.
b. Include R code in an Appendix, not in the main body of the report.
c. Submit your report through Blackboard.
d. Marks will be given for good explanation of your work and for
presentation.
e. The emphasis is on the quality of your work, rather than quantity. You
can achieve more marks by applying a few techniques well, rather than
doing many poorly.
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APPENDIX A
This data you will use in your project is constructed from a Lending Club data
set of personal loans (www.lendingclub.com).
Each observation refers to a single personal loan originated in the USA and
includes application variables as listed in the table below, along with outcome
variables indicating default.
Variable Description
acc_now_delinq The number of accounts on which the borrower is now delinquent.
addr_state The state provided by the borrower in the loan application
annual_inc
The self-reported annual income provided by the borrower during
registration.
avg_cur_bal Average current balance of all accounts
chargeoff_within_12_mths Number of charge-offs within 12 months
def_flag
Indicates where loan defaults, with three outstanding missed
payments, within the past 2 years
delinq_2yrs
The number of 30+ days past-due incidences of delinquency in the
borrower's credit file for the past 2 years
delinq_amnt
The past-due amount owed for the accounts on which the
borrower is now delinquent.
dti
A ratio calculated using the borrower’s total monthly debt
payments on the total debt obligations, excluding mortgage and
the requested LC loan, divided by the borrower’s self-reported
monthly income.
emp_length_p
Employment length in years. Possible values are between 0 and 10
where 0 means less than one year and 10 means ten or more
years.
grade LC assigned loan grade
home_ownership
The home ownership status provided by the borrower during
registration or obtained from the credit report. Our values are:
RENT, OWN, MORTGAGE, OTHER
initial_list_status The initial listing status of the loan. Possible values are – W, F
inq_last_6mths
The number of inquiries in past 6 months (excluding auto and
mortgage inquiries)
int_rate Interest Rate on the loan
issue_d The month which the loan was funded
loan_amnt
The listed amount of the loan applied for by the borrower. If at
some point in time, the credit department reduces the loan
amount, then it will be reflected in this value.
mo_sin_old_rev_tl_op Months since oldest revolving account opened
mo_sin_rcnt_rev_tl_op Months since most recent revolving account opened
mo_sin_rcnt_tl Months since most recent account opened
mort_acc Number of mortgage accounts.
num_accts_ever_120_pd Number of accounts ever 120 or more days past due
num_actv_bc_tl Number of currently active bankcard accounts
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Variable Description
num_actv_rev_tl Number of currently active revolving trades
num_bc_sats Number of satisfactory bankcard accounts
open_acc The number of open credit lines in the borrower's credit file.
pub_rec Number of derogatory public records
pub_rec_bankruptcies Number of public record bankruptcies
purpose_p A category provided by the borrower for the loan request.
revol_bal Total credit revolving balance
term
Loan term. The number of payments on the loan. Values are in
months.
total_acc
The total number of credit lines currently in the borrower's credit
file
total_rev_hi_lim Total revolving high credit/credit limit
verification_status
Indicates if income was verified by LC, not verified, or if the income
source was verified
 Note that some of the descriptions of variables are ambiguous or unclear.
However, this is the only information about the data we have.