Ch 4 .Operational Risk Tools - Categorisation

Question 1

Why is creating a single, consistent data categorisation approach important for operational risk management?

Answer 1

• Provides a common language for the operational risk framework, including policy development, risk identification and quantification, scenario assessment, risk or loss event management, and the deployment of risk, control, and performance metrics.
• Large quantities of unstructured data cannot provide the necessary information for an operational risk manager to do their job effectively. Therefore, data needs to be organised in a scientific way, with a clearly defined hierarchy, to be useful.
• Data that is classified in a consistent way is easier to aggregate, analyse, and report than data which is unstructured and uncategorised.
• A well-defined and implemented categorisation scheme provides good control over data and helps to make sense of it, thereby supporting effective operational risk management.

• provides common language
• makes data clear
• Easier to aggregate and analyse
• Good control

Question 2

What is the difference between ‘taxonomy’ and ‘data categorisation’ in the context of operational risk management, and how do firms typically approach operational risk data categorisation?

Answer 2

• In this context, ‘taxonomy’ is used to describe the data attributes for risk events such as losses, near misses, and gains.
• On the other hand, ‘data categorisation’ or ‘data categorisation scheme’ is used to describe the many forms of data relevant to operational risk managers which need to be categorised.
• In terms of industry practice, there is no single standard for operational risk data categorisation that can be used by all firms for all purposes. Most firms either adapt an industry categorisation scheme (like the Basel II standard) to meet their internal needs, or create their own bespoke categorisation scheme.
• If they choose the latter, they often map their own bespoke data scheme to regulatory or other external/industry definitions. This kind of mapping of firms’ own internal data categories to regulatory or industry categories is an important feature of the operational risk data framework.

• Taxonomy - data made up of different attributes e.g losses,near misses and gains
• Data categorisation - many forms of data that need to be grouped.

Question 3

What are the benefits of a successful data categorisation scheme in operational risk management?

Answer 3

Scaling/Aggregation: It allows for accurate and appropriate scaling or aggregation of data, given that the underlying source data is homogeneous and classified consistently.

Completeness: It provides assurance to risk managers and other control functions that the datasets they are examining are complete.

Internal Reporting: It facilitates the grouping of common data sets in a meaningful manner, enabling comprehensive, accurate, and informative reporting. It also allows for comparisons between different types of data using common standards.

Regulatory Reporting: It enables firms to meet regulatory obligations by arranging their data in accordance with the structure demanded by regulators.

**Benchmarking: **It allows for internal and external benchmarking, providing a different perspective for risk management. This requires the data sets of the respective entities to be comparable, i.e., categorised using the same data attributes. It also supports the analysis and confirmation of initial assumptions versus the actual data collected.

• scaling and aggregation
• Internal reporting
• regulatory reporting
• Bench marking
• Completeness

Question 4

How much detail (granularity) should be included in a data categorization scheme, considering the balance between capturing specific information and maintaining a manageable structure?

Answer 4

The level of detail (granularity) depends on the intended use of the data. Here are the key factors to consider:
* **Purpose of the Scheme: **Define data use upfront (accident rates vs. credit card risks) to determine detail needed.
* Specificity vs. Manageability: Capture relevant details, but avoid excessive levels that become difficult to manage or lose broader context. Stop adding child levels when details become specific (branding, pricing).
* Future-Proofing: Consider potential future uses of the data and make informed judgments about detail based on the scheme’s purpose.
* Adaptability: Design the scheme to be flexible and allow adding new categories for unforeseen data types.

• Purpose of the scheme
• Depends on how one is to manage data
• future proof
• Adaptability

Question 5

What are the common elements included in a data categorization scheme for operational risk, and how should they be defined for optimal effectiveness?

Answer 5

Here are some of the key elements and considerations for defining them in a data categorization scheme:

• Process Types:
  Define common, high-level activities across the firm (e.g., customer onboarding, transactions).
• Risk Types:
  Consider industry-specific risks (e.g., credit card fraud for banks).
  * Control Types:
  Categorize controls based on purpose (preventative, detective, corrective) or nature (physical, procedural, information security, etc.).
• Industries:
  Use existing industry classification structures from national/international bodies (e.g., World Bank) for consistency.
  * Business Lines:
  Consider adapting the Basel II business line structure (e.g., retail banking, wealth management) to your specific needs.
  * Products/Services:
  Define product/service categories within business lines or industry types.

• Process types
• Business lines
• Industries
• Control types
• products/services

Question 6

How does the bow-tie model inform the design of a data categorization scheme for risk assessment?

Answer 6

The bow-tie model emphasizes the distinction between causes, events, and impacts of risks. An effective data categorization scheme should support analysis of each of these components.

Components of the Bow-Tie Model:

Causes: Factors that can trigger an event (e.g., people, processes, systems failures).
Events: Occurrences resulting from causes (e.g., data breach).
Impacts: Consequences of events (e.g., financial loss, reputational damage).

Data Categorization Scheme:

• Needs to capture data relevant to each component of the bow-tie model (causes, events, impacts) in the diagram.
• Should have sufficient granularity to enable proper analysis of cause-event-impact relationships.
• If the data scheme hinders analysis (e.g., due to lack of detail), it needs to be refined to better align with the bow-tie model.

• components of bow tie - cause,event and impact. Data categorisation shd support these components.

Question 7

How does a data categorization scheme integrate with various components of an operational risk management framework?

Answer 7

A well-defined data categorization scheme acts as the foundation for various activities within an operational risk management framework. Here’s how it connects to different elements:

RCSA (Risk & Control Self-Assessment): Classify risks (type, customer, channel), controls (type, process), and assessments (process, cause, quantification).

**Risk & Control Indicators: **Link indicators to risk types/categories and potentially causal types.

**Risk Events: **Record events using data from all scheme elements (risk types, processes, products/services, etc.).

**Scenario Analysis: **Define categories for potential risk situations (types, processes, channels, causes, controls).

Capital Estimation: Ensure categorized data from RCSA, scenarios, indicators, and loss events aligns with the scheme.

Other Applications: Use the scheme for consistent data presentation in business continuity management, information security, and management reporting.

• RCSA- by classsfying risk types
• Risk & control indicators - link indicators to risk types/categories
• Risk events
• Scenario analysis
• Capital estimation
• Business continuity mgt

Question 8

What are the main challenges in creating and using a data categorization scheme for operational risk management?

Answer 8

Scope/Definition:

• Decide on the breadth of the scheme (e.g., operational risk only vs. broader risk management).
• Ensure clear and unambiguous definitions for each element to avoid misclassification.
• Consider the viewpoint used for categorizing risk events (e.g., cause-event-impact chain).

Granularity:

• Balance the need for unambiguous data with user-friendliness.
• Use more granular levels for critical elements (e.g., risk category, controls) and higher levels for others (e.g., risk type, geography).

Buy-in:

• Obtain management support and promote the scheme’s benefits.
• Use business-familiar language for labels and categories.
• Consider allowing divisional customization while maintaining overall consistency.

Maintenance and Data Quality:

• Regularly update the scheme to reflect changes in the business environment.
• Train staff on using and applying categories effectively.

• Management buy in
• Maintenance and data quality
• Granularity
• Scope and definition