This book is not a training manual on how to build Artificial Intelligence (AI) models. It is intended as an executive’s guide to applying AI technologies to transform risk management into a proactive management tool for informed decision-making and exploiting opportunities.

It expands on book 1 -“Mastering 21st Century Enterprise Risk Management”, and assumes readers understand event driven and objective base risk management. This includes the use of scenario analysis, casual mapping, horizon scanning, and risk aggregation. If you are not comfortable with these techniques, I strongly suggest reading my previous book before proceeding. As an Executive’s Guide, it covers these topics at a high level, so it is an easy read.

AI, although a technical subject, it is not difficult to understand from an application and management perspective. I have taken the approach that the reader does not have prior technical IT or AI background or knowledge. Hence, I have used everyday language to explain the techniques and concepts.

The title refers to AI, but more accurately I am referring to the whole raft of disruptive technologies. This general term covers the swath of technologies that are changing the face of the world as we know it. From bitcoin to drone pizza delivery these new technologies are IT based, use an augmented intelligence, and are most likely “cloud” dependent. End-point delivery might be via a local hardware device, but the solutions rely on distributed or massive processing power facilitated by “the cloud”.

These disruptive technologies open a completely new level of ability to risk management for identifying, evaluating, and monitoring risk. Also for control and mitigation as well as training and reporting. My Top 10 Disruptive Technologies that will change Risk Management in the 2020s are:

1. Probabilistic Modelling – to mirror real-world uncertainty and aggregate the effects of risk on strategic objectives.

2. Knowledge Graphs – to map risk network relationships to identify and understand sources of risk.

3. Neural Networks (aka Deep Learning) - to classify risk, identify patterns in data and images, and recommend courses of action.

4. Big Data& Predictive Analytics - to build risk collateral, identify trends& evolving risk, anomaly detection, and threat management.

5. IoT – Intelligent Things - to monitor changes in environmental factors in real-time, and using streaming analytics to identify stress and internal risks.

6. Virtual& Augmented Reality - to gain a quantum leap in staff training, building a robust risk culture, and provide real-time expertise to critical tasks.

7. Natural Language Processing (NLP) - providing text analysis to identify regulatory compliance issues and sentiment analysis to monitor behaviour.

8. Robotic Automated Processes (RPA) – AI infused workflows to augment human processes integrating re