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Gregory M. Carroll: How AI will transform Risk Management

Updated: Oct 2, 2021

Greg's book “Risk Intelligence – How AI can transform Risk Management” is due for release 5-Oct-2021, but it is available on Amazon at Risk Intelligence: How Artificial Intelligence can transform Risk Management (The Future of ERM Book 2) eBook : Carroll, Gregory M.: Kindle Store


Although referring to AI, more accurately it is the whole raft of disruptive technologies changing the face of the world as we know it. 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 in training and reporting. I will discuss my Top 10 Disruptive Technologies that will change Risk Management in the 2020s:

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 research and risk-based decision-making at the coalface.

9. Blockchain Distributed Trust Systems – that will transform everything from cybersecurity and supply chain risk to making individuals responsible for their carbon footprint.

10. Bayesian Decision Networks – applying expert experience and probabilistic modelling to risk scenarios to identify the most likely outcome of complex events.

Gartner predicts that by the end of 2024, 75% of organisations will move from piloting AI to its mainstream adoption. Risk Management must follow suit to stay relevant.


Speaker biography

Greg has extensive experience in AI-based risk management with the likes of Aust Dept of Defence and Victorian Infectious Diseases Labs. Author of “Risk Intelligence” and “Mastering 21st century Enterprise Risk Management”, Greg is a strong advocate for applying disruptive technologies to risk management.

As founder and Director of Fast Track (Aust), Greg has implemented both Machine Learning and Risk Management solutions for multinationals like Motorola and Serco. This includes pro-active AI and risk analytics solutions using deep learning to classify risk, random forests for scenario analysis and Bayesian networks for risk aggregation.

Since doing a Graduate Diploma in Computer Simulation (Operations Research) at Swinburne University, he has been heavily involved with computer-based decision tools. Greg also has a Certificate in Machine Learning from Stanford University, in Data Science from the University of Michigan and is a Microsoft MCP full stack developer.


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This article is a summary and commentary of the speakers and topics covered at this event, organised by the Institute of Risk Management’s Innovation Special Interest Group, in partnership with: Satar