代写FM 9528留学生、代做Risk Analytics、Java，c/c++，Python程序语言代做

日期：2019-11-13 05:11

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

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to the notebook must also be included. Instructions how to export to PDF can be found here:

https://stackoverflow.com/questions/52588552/google-co-laboratory-notebook-pdf-download

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

such as cover page, executive summary, title page, table of contents, tables, figures, in-text citations

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

your name does not appear on any part of your assignment.

Submission Deadline: November 14

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

detection software under license to the University for the detection of plagiarism. All papers

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.