The Asset Management Group did not have risk visibility for all assets, a structured way of prioritising interventions or have a method of quantifying the corporate risk appetite. We required the ability to set asset risk targets, assess gaps, identify interventions, and implement interventions in a structured manner for all assets. Our solution was to add all 2.7M to a single risk matrix and use it as a tool to quantify the corporate risk appetite. This was also intended to simplify our investment justification.

We weren’t quite able to bring the goal to fruition in accordance with the original goal. But in this session you'll hear what we did achieve, what we would do differently, and explain what we think is possible to bring this idea to maturity.
In this presentation we will discuss many of the contributing factors and as well as the successes. This is a lesson’s learnt presentation on what we have done to date and what we need to do in the future.

• Adding 2.7M assets in a single risk matrix – semi-qualitative
• Different models for different applications – incorporating machine learning, statistical analysis and heuristic engineering models
• Accessing data – challenges in joining disparate asset sources, data in non-electronic format, reliance on unstructured data
• Quantified and economic justification

Key takeaways include;

1. Stakeholders need to be engaged and on board to create trust in model outputs
2. System needs to be fully supported – resourced and governed - to enable completion and continual improvement
3. Iterative development is good – pivot faster if not working – created in ‘Scrumban’ style with many competing priorities