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FM 9528 - Banking Analytics Coursework 2
Coursework 2- Credit Risk Analytics
Lending Club is a well-known peer-to-peer (P2P) lenders operating
in North America. Its business model is to let potential investors diversify their risk by splitting their
investments across multiple loans. To add transparency to their models, Lending Club has been
realising their lending history in its entirety, available for the period 2007 to 2018 for the United
States, along with loan performance for them. The dataset has approximately 2.3 million loans and
around 110 variables.
In this coursework, you will develop a fully compliant advanced IRB model from the data they make
available, from the raw data to the level 2 calibration, using what you have learned in the lectures.
The objective of the coursework is to estimate the capital requirements for Lending Club were they
to be regulated (they are currently not).
1. (30%) Prepare the dataset to make it ready for a credit scoring application model and for a
Loss Given Default model and calculate the default variable and the workout LGD variable.
Discuss all your decisions, particularly focus on which variables can be used to predict PD,
which ones to predict LGD, which ones are variables used to construct your objective
variables, and which variables cannot be used. Note the variable “grade” is not a predictive
variable, for example, as it includes the business logic by Lending Club.
2. (35%) Construct a scorecard which can model the probability of default for the loans. As you
do not have information regarding 12-month performance, use the status “Chargeoff” as
the objective variable from variable “loan_status”. Discuss your choice of variables, your
decisions regarding Weight of Evidence and other transforms you choose to make, and your
final performance. Discuss the variable importance. How many variables do you
recommend using?
3. (35%) Construct two Loss Given Default models, using Random Forest and XGBoosting, over
the defaulted loans only. Use cross-validation to determine your optimal parameters, if
necessary, discuss the variable importance and the accuracy metrics you see relevant.
Compare the performance of both models and discuss your findings. Discuss the variable
importance of both models. Do they agree? Why? Apply this model to the non-defaulters
and discuss the average estimated LGD values over these cases.
4. (Extra credit, 20%. Maximum score 100%) Using the monthly macroeconomic information
you consider relevant (see for example https://stats.oecd.org/Index.aspx), calibrate a longrun
PD and downturn LGD model for the loans granted regressing your monthly Lending
Club’s PDs (from your objective variable) per rating (from the variable ‘grade’) against the
macroeconomic variables. Use the long-term forecasts you can find online from reputable
sources (for example the OECD) for your long-term calibrated values. If you cannot find
them, assume a value which makes sense to you and explain why. For the downturn values,
select the worst month GDP-growth-wise and use those macroeconomic values.
Conditions of the coursework
Software: You must use Python to run the numerical calculations over your portfolio. A copy of your
jupyter notebook must be attached to the coursework as an appendix in readable format, and a link
FM 9528 - Banking Analytics Coursework 2
to the notebook must also be included. Instructions how to export to PDF can be found here:
Word Limit: 2000 words +/-10% either side of the word count is deemed to be acceptable. Any text
that exceeds an additional 10% will not attract any marks. The relevant word count includes items
and section headings, if used. The relevant word count excludes your list of references and any
appendices at the end of your coursework submission.
You should always include the word count (from Microsoft Word, not Turnitin), at the end of your
coursework submission, before your list of references.
Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Course Code,
Assignment Title, Word Count. This assignment will be marked anonymously, please ensure that
th, 23:59.
Turnitin Submission: The assignment MUST be submitted electronically via OWL. All required
papers may be subject to submission for textual similarity review to the commercial plagiarism
submitted for such checking will be included as source documents in the reference database for the
purpose of detecting plagiarism of papers subsequently submitted to the system. Use of the service
is subject to the licensing agreement, currently between The University of Western Ontario and
Turnitin.com (http://www.turnitin.com).
Late Submission: Late submissions are possible up to a week after deadline. There is a 10% penalty
per day of late submission subtracted directly from the final mark. Submissions after the 7 days are
not accepted and will be considered a non-submission.

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