Chapter 4 Key Learning Questions Flashcards
- What are some of the key benefits of data categorisation?
Some key benefits include: enabling scaling/aggregation of data; providing completeness by identifying gaps in data collection; facilitating internal reporting by enabling grouping of common data sets; enabling regulatory reporting by structuring data to meet regulatory templates; enabling benchmarking against peers by ensuring comparable data categorization schemes; and reducing data interpretation variability by providing common definitions and standards.
- What industry data categorisation options are available to firms?
Firms can choose to adapt an existing industry categorisation scheme, such as the Basel II standard taxonomy, or a scheme from an industry body. Alternatively, firms can create their own bespoke categorisation scheme and map it to external standards to enable benchmarking and data sharing.
- What are some of the key features of a good data categorisation scheme?
Key features include: homogeneity within categories, sufficient granularity to enable unambiguous low-level categories, simplicity and intuitiveness balanced against granularity and usability, comprehensiveness across the firm’s activities and data needs.
- What types of operational risk data are typically structured using a data categorisation scheme?
Typical categories include: process types, risk types/categories, control types, industries, business lines, products/services, customers/clients, distribution channels, geographies and causes.
- How can the bow-tie model facilitate creation of a data categorisation scheme?
The bow-tie model distinguishes causes, events and impacts. This forces appropriate categorization to support analysis of each component. The related data scheme must enable analysis of the full cause-event-impact relationship.
- What are some of the common ways in which a data categorisation scheme is applied to operational risk activities?
A categorisation scheme is commonly applied to: risk and control self assessments through risk registers and mapping controls; risk indicators by linking to risk types/categories; loss data by capturing causes, events and impacts; scenario analysis by describing situations, causes, controls; capital modelling via inputs from self assessments, scenarios, indicators, loss data.
- What are some of the key challenges which need to be considered when developing a data categorisation scheme?
Key challenges include: scope/boundary definition; achieving appropriate granularity balance; gaining business adoption and senior management buy-in through intuitive terminology; ongoing maintenance and applying categorization changes; data quality through consistent staff application and avoiding shortcuts; determining optimal location of categorisation process balanced against business knowledge.
