Visualising Operational Risks

Operational risks results differ from other types of financial risk results such as market or credit risk. The latter are defined in numerical ratio terms – examples include profit and loss, value-at-risk and exposure. We can add these measure together naturally.

However, operational risk results are usually expressed in ordinal terms. For example, an operational risk and controls self-assessment (RCSA) estimates the inherent risks, assesses the controls that are in place to avoid and mitigate these risks and then estimates the residual risks after these controls are applied. The effectiveness of each control is also estimated – typically on a scale from adequate, inadequate, vulnerable and worst-of-all not controlled.

There are a set of 7 industry standard well defined event type categories; internal fraud, external fraud, employment practices, clients & products, damage to physical assets, business disruption and execution & delivery. Each of these is broken down into sub-categories. For example, internal fraud splits into unauthorised trading, bribery and others. Furthermore, many parts of the bank submit a RCSA assessment and these may be organised into a hierarchy.

Risk Managers want to view these as heat maps. They would view initially at an aggregated level, for example, at event type category and across all regions. The aggregated values should be the “worst case” of the detail values so if a department has estimated low for misappropriation of assets, medium for mismarking and high for bribery then the internal fraud aggregated score would be high.

The snapshot below is a typical heatmap of RCSA scores, mocked up in Excel. The columns list the inherent risks, mitigating controls these residual risks. These are broken down by region. The rows list the standard operational risk event types

Mockup RCSA Heatmap

The data for this heatmap is a table – first few rows below.

Mockup RCSA Data

Let’s build this heatmap in Power BI Desktop. Our first problem is that these are ordinal results. L, M, H, VH values represent low, medium, high and very high scores for risks. Similarly, A, I, V, NC, values represent adequate, inadequate, vulnerable and not controlled for effectiveness of controls. So we add another dataset that helps us map these ordinal scores to a numeric weight to indicate the order of these scores. The actual values of those weights don’t matter, just the relative values.

Mockup RCSA Weights

We’ll merge these two datasets in the Query Editor to get a combined data set like this below

RCSA Query Editor Merged Data

 

We then create two measures

WorstCaseWeight = MAX(RCSAData[Weight])

This measure aggregates as worst-case manner as required.

WorstCaseScore = SWITCH([WorstCaseWeight], 10, “L”, 20, “M”, 30, “H”, 40, “VH”, 110, “A”,120, “I”, 130, “V”, 140, “NC”)

This measure maps our worst case weight into the appropriate ordinal score. Power BI does not allow a string field in the values well so we can’t add our score values directly but it does allow any measure – even if that measure returns a string.   This is very useful for us.

Now we can visualise our heatmap both at an aggregated and a detail level.

Mockup RCSA Heatmap in PBID - summary

Mockup RCSA Heatmap in PBID - detail

 

This gives us basically what we need with the exception of conditional formatting of the cells. We have a heat map without the “heat” which is unsatisfactory. What we need is to be able to map these categorical scores to colours and apply these colours to the cell background. This is not yet possible for the matrix visual, or indeed for any Power BI visual but I hope that this will be a features of upcoming update – or that someone will build a heat map custom visual.