MANG6556 Credit Risk & Data Analytics

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COURSEWORK BRIEF:

Module Code: MANG6556
Assessment: Individual Coursework
Weighting: 100
Module Title: Credit Risk & Data Analytics
Module Leader: Dr Huan Yu
Submission Due Date: 10 Jan 2025 @16:00
Word Count: 2500

Method of Submission: Electronic via Blackboard Turnitin ONLY  (Please ensure that your name does not appear on any part of your work)

Any submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see below), unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.

Days Late:
 Mark:
1
 (final agreed mark) * 0.9
2
 (final agreed mark) * 0.8
3
 (final agreed mark) * 0.7
4
 (final agreed mark) * 0.6
5
 (final agreed mark) * 0.5
More than 5
 0

This assessment relates to the following module learning outcomes:

A. Knowledge and Understanding
A1. Understand the potential of CRISP-DM and data analytics, particularly in the retail lending sector.

A2. Demonstrate a critical understanding of different types of data analytics methods and the problems they can solve.

A3. Interpret the output of statistical techniques used for the main data analytics applications.
B. Subject Specific Intellectual and Research Skills

B1. Identify the statistical models appropriate for analysing the various decisions that confront a data analyst in different industries.

B2. Work with software to develop data analytics solutions, such as predictive scorecards, clusterin models, and different types of regressions.

B3. Assess the relevance of statistical package outputs to the decisions being addressed.

C. Transferable and Generic Skills

C1. Critically analyse practical difficulties that arise when implementing retail credit risk models; understand the cross-fertilisation potential to other business contexts (e.g., fraud detection, marketing, CRM, etc.).

C2. Demonstrate an ability to use world-class software and to interpret its output in the relevant techniques.

C3. Manage time and tasks effectively in the context of individual study.

Coursework Brief:

Question 1 (70 marks)

The dataset ‘Credit data.xlsx’ contains data on 10,000 borrowers and whether they subsequently experienced serious delinquency (see variable ‘SeriousDlqin2yrs’). Assume the lender now wishes to use this data to build a credit scoring model that predicts serious delinquency based on the other variables. The dataset contains the following variables:

Variable Name
Description
SeriousDlqin2yrs
Person experienced 90 days past due delinquency or worse
RevolvingUtilizationOfUnsecuredLines
Total balance on credit cards and personal lines of credit except real estate and no installment debt
like car loans divided by the sum of credit limits
age
Age of borrower in years
NumberOfTime30-59DaysPastDueNotWorse 
Number of times borrower has been 30-59 days past due but no worse in the last 2 years.
DebtRatio
Monthly debt payments, alimony,living costs divided by monthy gross income
MonthlyIncome
Monthly income
NumberOfOpenCreditLinesAndLoans
Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)
NumberOfTimes90DaysLate
Number of times borrower has been 90 days or more past due.
NumberRealEstateLoansOrLines
Number of mortgage and real estate loans including home equity lines of credit integer
NumberOfTime60-89DaysPastDueNotWorse 
Number of times borrower has been 60-89 days past due but no worse in the last 2 years.
NumberOfDependents
Number of dependents in family excluding themselves (spouse, children etc.)

1.1 Carefully pre-process the data set by considering the following activities (35 marks):
  • exploratory data analysis
  • missing value handling (if any)
  • outlier detection and treatment (if any)
  • categorisation of the continuous variables (if deemed useful)
  • Weights of Evidence coding (note that some additional coarse classification might be needed).
  • Splitting the data set into a training and test set.
1.2 Estimate a scorecard using a logistic regression classifier and report the following (35 marks):
  • The most important variables
  • The impact of the variables on the target
  • The performance of the model. Use various performance metrics and discuss their relationship if any.
  • Result of scorecard.
  • Compare this scorecard with the results of a Random Forest. Discuss your results.
  • Why do banks typically use Logistic Regression as their base classifier? What do banks win and lose by doing this?

Please carefully report the various steps of your methodology and discuss your results in a rigorous way!

NOTE: It is unlikely that different students will come up with the exact same parameter estimates. Special consideration will be given to submissions whose estimates are identical.

Question 2 (30 marks)

Find an academic paper published in 2021 or later (based on online or print publication date) discussing a real-life application of data analytics. It is important that the dataset analysed in the paper consists of real-life (not artificial) data. The publication outlets in which to look for a suitable paper are:


  • Management Science
  • Operations Research
  • INFORMS Journal on Computing
  • INFORMS Journal on Applied Analytics
  • Journal of Machine Learning Research
  • European Journal of Operational Research
  • Production and Operations Management
  • Manufacturing & Service Operations Management
  • ICDM (The IEEE International Conference on Data Mining)
  • NeurlPS (Conference on Neural Information Processing Systems)
  • KDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining)


The other journals which are not on the list are not acceptable.

2.1 Once you have found an appropriate paper, report the following in separate subsections (15 marks):

  • Title, authors, and complete citation (e.g., journal name, volume/issue, year, …)
  • The data mining problem considered
  • The data mining techniques used
  • The results reported
  • A critical discussion of the model and results (assumptions made, shortcomings, limitations, …)
2.2 Apply the methodology you reviewed into the dataset of ‘Credit data.xlsx’ and report the analytic steps, model performance, and business implications. (15 marks)

Make sure you demonstrate that you understand what the article is all about and are able to provide a critical discussion.

Do not copy and paste from the article. Using Turnitin, this will be easily detected!

NOTE: The reviewed methodology should be different from methods applied in Question 1.

Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See ‘Weighting’ section above for the percentagethat this assignment counts towards your final module mark.

Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceedsan 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 courseworksubmission, before your list of references.

Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module 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.

References: You should use the Harvard style to reference your assignment. The library provide guidance onhow to reference in the Harvard style and this is available from: http://library.soton.ac.uk/sash/referencing

Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL assessments.

Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via theindividual module on Blackboard. Further guidance on submitting assignments is available on the Blackboardsupport pages.

It is important that you allow enough time prior to the submission deadline to ensure your submission isprocessed on time as all late submissions are subject to a late penalty. We would recommend you allow 30minutes to upload your work and check the submission has been processed and is correct. Please make sureyou submit to the correct assignment link.

Email submission receipts are not currently supported with Turnitin Feedback Studio LTI integrations, howeverfollowing a submission, students are presented with a banner within their assignment dashboard thatprovides a link to download a submission receipt. You can also access your assignment dashboard at any timeto download a copy of the submission receipt using the receipt icon. It is vital that you make a note of your

Submission ID (Digital Receipt Number). This is a unique receipt number for your submission, and is proof ofsuccessful submission. You may be required to provide this number at a later date. We recommend that youtake a screenshot of this page, or note the number down on a piece of paper.

The last submission prior to the deadline will be treated as the final submission and will be the copy that isassessed by the marker.

It is your responsibility to ensure that the version received by the deadline is the final version,resubmissions after the deadline will not be accepted in any circumstances.

Important: If you have any problems during the submission process you should contact ServiceLineimmediately by email at [email protected] or by phone on +44 (0)23 8059 5656.

Late Penalties: Further information on penalties for work submitted after the deadline can be found here.

Special Considerations: If you believe that illness or other circumstances have adversely affected your academicperformance, information regarding the regulations governing Special Considerations can be accessed via the Governance and Policies landing pages: Regulations Governing Special Considerations (including Deadline Extension Requests) for all Taught Programmes and Taught Assessed Components of Research Degrees 2023-24 | University of Southampton

Extension Requests: : Extension requests along with supporting evidence should be submitted to the Student
Office as soon as possible before the submission date. Information regarding the regulations governing extension requests can be accessed via the Governance and Policies landing pages: Regulations Governing
Special Considerations (including Deadline Extension Requests) for all Taught Programmes and Taught Assessed Components of Research Degrees 2023-24 | University of Southampton
Academic Conduct & Responsibility: Please note that you can access Academic Conduct & Responsibility Guidance for Students via the Quality Handbook: http://www.southampton.ac.uk/quality/assessment/academic_integrity.page?. Please note any suspected cases of Academic Responsibility Conduct will be notified to the Academic Conduct Officer for investigation.

In 2023/24, the most common reasons for a breach of the regulations governing Academic Responsibility Conduct on your programme were:

Breach
 How to avoid
Plagiarism – using the work, words, or ideas of another without properacknowledgement. Thisincludes citing work that you haven’t read.
- Always cite your sources.
- Only cite what you have read and used.
- “Direct quotes must be in quotation marks” with a page number if applicable.
- If you read about the work of another in a source, say ‘cited in’ and cite where you read it (see here for more info).
Collusion – Collaborating withothers in an unauthorized way to produce academic workmeant to be done independently.
-Unless permitted in a group assignment, don’t work with/alongside others.
- Don’t share your work with others.
- Ensure you are clear on where the line is. If in doubt, don’t do it.
External authorship – Obtaining or attempting to obtain unauthorized input fromanother person or service for academic work, e.g GenAI
- Ensure you are clear on if you are permitted to use GenAI.
- Ensure your work is always your own.
- Never send your work to others or upload it to a website.
-Keep records of your work including notes, drafts, and reading.

Penalties for the above include mark reduction, resubmitting for a capped mark, or a ‘0’ for themodule.

If you are in any doubt, please ask.

Further learning and advice can be found in the Academic Conduct & Responsibility Toolkit, and the Library Website.

Feedback: Southampton Business School is committed to providing feedback within 4 weeks(University working days). Once the marks are released and you have received your feedback, you can meet with your Module Leader / Module Lecturer / Personal Academic Tutor to discuss the feedback within 4 weeks from the releaseof marks date. Any additional arrangements for feedback are listed in the Module Profile.

Student Support: Study skills and language support for Southampton Business School students is available at: http://www.sbsaob.soton.ac.uk/study-skills-and-language-support/.

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