COMP0044 Coursework assignment: Operational Risk Measurement for Financial Institutions, LGU, Singapore

You should select one of the following topics and write a report of up to 6000 words about it.

Topics:

1) Scenario analysis, assessment of rare events applied to fictitious but realistic cases, or real cases:

a. Probability of occurrence (range), range of impacts, sensitivity analysis with more or less controls and exposure

b. Requires MC simulations or equivalent

c. Requires the understanding of the drivers of likelihood and impacts of the scenario(s) in scope

d. Applicable to various types of operational risk: cyber security risk, thirdparty vendor risk, compliance risk climate risk, sanitary risks…

2) Model risks and sensitivity analysis depending on model choices

a. Based on fictitious generated data

b. Requires good knowledge (or fast learning) of statistical distributions to use in LDA modeling and stats background

c. Interpretation and implications of the findings for model risk

3) Machine Learning / NLP : operational risk events and risk communication

a. Text analysis on risk communication and / or incident reporting for (either): Incident categorization, Language analysis, Identification; identification of KRIs / signs of increased risks

b. Requires good understanding of operational risk + capacity to code in Python

c. Touches upon a growing field in ORM

Requirements:

• Understand the intuition behind the theory
• Understand the principles and assumptions over the formulas and methods
• Good enough is: what are in the notes and comments during lectures
• Excellent is: creativity around concepts, recommended readings and relevant application to risk management & measurement

Structure of the report:

1. Context and Motivation

•  Why is the topic important
• What is the general issues at stake / researched

2. Review of the literature

•  Previous publication and state of the art on the topic

3. Methodology

• Models and approaches used to answer the research question

4. Data used

• Data source and data training

5. Results and Interpretation

•  Results obtain and comments on their insights and limitations

6. Further challenges and research developments

• Avenues for further research and extensions to the work being done

7. Conclusions