COMP0044 Operational Risk Measurement for Financial Institutions

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Operational Risk Measurement for Financial Institutions



Module delivery

1819/A7U/T1/COMP0044 Masters (MEng)

Related deliveries

1819/A7P/T1/COMP0044 Postgraduate

Prior deliveries



Masters (MEng)

FHEQ Level


FHEQ credits



Term 1

Module leader

Chapelle, Ariane


Chapelle, Ariane

Module administrator

Ball, Louisa


The module aims to familiarise students with key concepts in the measurement and management of operational risk in the financial services. It will help them to understand the current issues and challenges faced by the sector, from a methodological, regulatory and systemic standpoint. By detailing the most current debates in the field, the module aims at allowing the students to subsequently become positive agents of solutions in the market place and in research in operational risk measurement and modelling.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. Discuss, select and apply the relevant methods to address issues in the assessement, measurement and aggregation of operational risk exposure.

On successful completion of the module, students will have a sound understanding of:

  1. the applications of essentials of data analysis and statistical estimation to operational risk measurement;
  2. the methods of scenario analysis, stress testing and regulatory capital assessment;
  3. the limitations of operational risk modelling and the ways to address them;
  4. some of the essential features of operational risk management in financial institutions and how quantification can support decision-making.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MEng Computer Science (International Programme) (Year 4)
  • MEng Computer Science (Year 4)
  • MEng Mathematical Computation (International Programme) (Year 4)
  • MEng Mathematical Computation (Year 4)


There is no formal pre-requisite for the formal. However, students with an economic or financial background tend to understands the concepts convered more easily, since the module applies to activities performed in the financial industry.

Equally, students with a statistical background will be adequately equipped to understand easily the lectures relating to estimation and distributions. 


The module is intended to introduce applied statistics to operational risk measurement and management in the financial services. The course is primarily focused on the techniques, impacts and benefits of using relevant statistical and analytical methods to support effective risk management in banks and insurance companies.

The syllabus consists of the following parts:

Operational Risk Scope and Regulation

Regulation on operational risk, Capital and consequences for the financial industry. Large events and loss overview.

Operational Risk data analysis

Internal and External loss data, distribution, tail risk, reporting and threshold and cut-off mix between internal and external data for loss distribution.

Scenario analysis and stress testing

Scenario identification and assessment process; Probabilities of rare events; Fault trees and event trees; Mixing quantitative and qualitative data. Stress testing Capital and financial robustness. Bayesian Techniques in operational risk

Risk correlations and indicators

Identification and selection of risk drivers. Correlations and determinants of losses.

Risk aggregation and diversification

Aggregating data from different sources, loss generating mechanisms, scaling and filtering of data. Copula-based approach for risk diversification; extreme value theory. Remaining issues in operational risk modeling.

Risk Reporting

Communicating risk assessment outcomes. Supporting decision-making through risk reporting.

An indicative reading list is available via


The module will be delivered through classroom-based lectures. On occasions, scholars and top practitioners in the field will be invited as guest speakers for an hour in the course.

Besides the lectures, students will be asked to read additional references, academic articles and book parts. Those readings will be particularly useful to prepare their course work.

Students will get to work on real industry cases for class exercises and individual assignments.


This module delivery is assessed as below:



Weight (%)



Individual or group report




In-class test one (1 hour)




In-class test two (1 hour)



In order to pass this Module Delivery, students must:

  • achieve an overall weighted Module mark of at least 50.00%;

AND, when taken as part of MEng Computer Science and MEng Mathematical Computation:

  • achieve a mark of at least 40.00% in any Components of assessment weighed ≥ 30% of the module.

Where a Component comprises multiple Assessment Tasks, the minimum mark applies to the overall component.