Understanding Metrics within the Risk Cockpit

Understanding Metrics within the Risk Cockpit


February 22, 2021

There is a well-established business adage, “you can not manage, what you can not measure”. Within the KRM22 Enterprise Risk Cockpit, Metrics are the primary item type for measurement.

Metrics are used to capture data from operational systems, external data sources and manually. As data comes into the Risk Cockpit, thresholds are applied to generate a normalised metric score and RAG or RAGAR status.

Metrics are directly and indirectly related to other high-level or ‘parent’ items within the KRM22 Enterprise Risk framework.

Within the KRM22 Risk Cockpit, Metrics are classified as either a measure or an indicator. The difference between measures and indicators are relatively simple; Measures are used to measure something while Indicators are used to indicate something.

Typically, Measures are used to capture important business facts which are either used as a direct input into decision-making or used as part of a calculation to generate Indictors. As an example, a measure might be the number of cleared trades yesterday.

In contrast, Indicators have derived data; ratio, percentage or similar, from other metric data. Within the Risk Cockpit, we have three different types of indicators;

  • Key Performance Indicators (KPIs) - indicate performance and are directly linked to Entities, Objectives, Processes, Initiatives, Systems, Venues and     Information Assets.
  • Key Risk Indicator (KRIs) – are directly linked to risks and indicate the level and changes related to risk severity.
  • Key Control Indicator (KCIs) – are directly linked to controls and indicate the level and changes related to control effectiveness.

As an example, an indicator (for risk of failure to clear all trades) might be Ratio of Trades Cleared vs Trades Cleared over the last 10 days at the same time.

Additionally, within the Risk Cockpit, both a measure and an indicator can be further categorised as;

Predictive – a predictive metric is designed to provide a‘ predictive indication’ of an outcome been realised; for example, number of uncleared trades 60 minutes prior to market close.

Outcome – an outcome metric is designed to measure if an outcome has been delivered or realised, for example, number of uncleared trades post-market close.

Informational – an informational metric is designed to provide a measurement of a metric which is of interest to key stakeholders, for example, firm share price.

Measurement is an integral part of any risk management framework. Therefore, Metrics are an important part of the KRM22 Risk Cockpit. However, a note of caution, measurement is relatively easy. It is relatively easy to gather data and it is relatively easy to become overwhelmed by Metrics.

When it comes to managing risk and building a risk-based culture, the ‘hard’ work must by done. Clear strategic objectives must be defined, risk appetite boundaries agreed and an understanding of how operational factors feed into the risk profile must be established to ensure measurement is done within a wider, strategic and operational context.