Chapter 15

Question 1

15.1: How does reverse stress testing differ from conventional stress testing?

Answer 1

Conventional stress testing examines the effect of low probability and high impact risk events on an organisation.
Examples include a large change in interest rates (such as a 2% increase in borrowing costs), a significant fall in asset
values or a large financial loss.

Reverse stress testing goes further, to examine destructive events that threaten the solvency of an organisation. The
idea is to determine the financial breaking point of an organisation.

Reverse stress testing helps an organisation to understand its vulnerabilities and how extreme future events may affect
its ability to continue as a going concern. By understanding these vulnerabilities, an organisation can prepare for and
hopefully avoid them.

Question 2

15.2: What is an emerging risk? Provide three current examples of emerging risks.

Answer 2

Emerging risks are one of the following:

• a completely new risk that has only recently emerged; or
• an existing risk that increases in significance (probability and impact).

Emerging risks are characterised by high levels of uncertainty and change. This means that they may be difficult to identify and assess based on current knowledge. Examples of emerging risk include:

• cyber risks, such as new types of hacking or denial-of-service attacks or attacks targeted at security weaknesses
  created by developments such as the ‘internet of things’;
• social media-related risks, including cyberbullying and social media legal risks;
• disruptive technologies and organisations that threaten existing business models (such as Uber, peer-to-peer
  lending and crowd-funding);
• extreme political change (such as Brexit); and
• climate change.

Question 3

15.3: Provide a definition of ‘big data’. How can big data be managed?

Answer 3

Big data is the term used to describe datasets that are so voluminous and complex that they cannot be managed using
traditional data-processing techniques. Data is created on a continuous basis and may be quantitative (numbers) or
qualitative (words and opinions).

Big data can be managed using sophisticated data-capture and analysis techniques that can cope with a variety of data
types and large volumes of rapidly increasing data. Big data techniques include the use of search algorithms and artificial
intelligence.

Data visualisation is an important aspect of big data management. A variety of visualisation tools can be used, including:

• 3D scatter plots
• Gantt charts
• heat maps
• networks
• stream graphs
• tree maps.