What new risk assessment methods are you using?

Think of scenario analysis a bit like you would if you were trying to play the game of “Which came first, the chicken or the egg.” A truly successful scenario analysis exercise has leaders who buy in to seeking an answer and stakeholders around the table trying to find answers. But in order to get the buy-in and have the right people at the table, you have done enough preparation to characterize the risk, and everyone needs to come to the table prepared with enough of the answers to think through to the end-game. A professional guide can be helpful, someone with a background in military, or someone who knows the risk area deeply. A benefit of scenario analysis is that the stakeholder engagement strengthens business judgement claims.

Scenario analysis is becoming particularly important in the context of climate risk. Climate scenario analysis is performed based globally recognized frameworks, such as IPCC. The results can be used to stress test portfolios of assets.

The energy industry faces an uncertain future with periods of excess power on the grid, shifting regulatory priorities, social pressure for cleaner energy, & demand for greater customer control from expert prosumers and vehicle-to-grid enthusiasts. Allowing for a consideration of what is possible through scenario planning will widen the industry view of the changing world, help teams go beyond straight-line future projections, and most importantly, provide a way to acknowledge & design for complex disruption ahead. Remember: seemingly unrelated external trends can & will fold together to create the new future. This disruption will reveal new opportunities & threats, as well as avenues for collaboration and co-creation.

To my understanding the scenario analysis is a crucial modern risk management technique as it helps organizations to anticipate and prepare for various potential future events and their impacts. By creating different plausible scenarios, businesses can identify vulnerabilities, assess potential risks, and develop effective strategies to mitigate or respond to those risks. This approach is especially valuable in today's dynamic and uncertain bsiness environment, enabling proactive decision-making and enhanced resilience.

Another risk assessment approach is the use of PRA, Probabilistic Risk Assessment. The approach actually models the structure and dependencies of elements. One application is used in the nuclear industry to model the safety performance of nuclear power plants in response to postulated events. It models the response of safety systems, operator human performance in decision making, etc. It showed the dominant generator of risk was not dependent upon the design of nuclear systems but rather a dependency upon external, non-nuclear systems and the assumed performance of operators. Simply said, the largest risk potential wasn’t the postulated “big events” but a combination of lesser, more probable events occurring in sequences.

MC simulation is widely used in many areas, particularly finance and investment. Through the MC simulation, the inherent uncertainty and complexity of the investment world are translated into a quantifiable risk profile. This allows investors to understand potential outcomes and make decisions that align with their risk tolerance and investment goals. It turns abstract uncertainties into concrete probabilities, providing a robust foundation for risk management.

Heatmap is useful tool for visualizing risks, but it is not a substitute for a thorough risk assessment. A thorough risk assessment is still needed to get adequate picture of the risks and to develop appropriate mitigation strategies.

Artificial Intelligence (AI) plays a significant role in enhancing risk analysis by providing advanced capabilities for data processing, pattern recognition, and decision-making. Here's how AI contributes to risk analysis: 1. Data Analysis and Processing. 2. Predictive Analytics. 3. Machine Learning Models. 4. Natural Language Processing (NLP). 5. Anomaly Detection. 6. Automation and Decision Support. 7.Scenario Simulation and Modeling. 8. Cybersecurity and Fraud Detection. 9. Portfolio and Investment Risk. 10. Supply Chain Risk Management.

New methods in Risk Management emerge with AI: 1. Predictive Analytics: You can use AI to forecast risks from past trends. Analyze market data to predict potential downturns. 2. Real-time Alerts: AI flags threats instantly. Set up immediate cybersecurity breach alerts. 3. NLP & Compliance: Let AI align your documents with laws. Check contracts for compliance regularly. 4. Computer Vision for Safety: Use AI to spot physical risks. Cyber risk is a high-risk area, start there. 5. Data-Driven Insights: AI can process vast amounts of data, as long as it's structured it can guide you with invaluable insights. Review AI reports before key decisions. Use AI for smarter, safer strategies.

Royal Dutch Shell used scenario-based risk assessments in the 1970s. One of those scenarios, unbelievable at the time, was the failure of the Soviet Union with the resulting action by Russia to flood the market with cheap oil and natural gas. That event later resulted in an over dependency of Europe upon a single, cheap supply controlled by a foreign source.

Second order thinking is a simple, yet often underutilized risk assessment technique. By examining not only direct outcomes but also their ripple effects, we can better predict challenges. Reflect on Hanoi: incentivizing rat catching unexpectedly led to rat breeding. Similarly, Blockbuster missed the digital wave, leading to its decline as Netflix surged. These examples highlight the essence of asking, "And then what?" This technique isn't solely for risk assesment. Nor only for leadership decisionmaking; it's universal. It’s about digging deeper, gauging actions and their broader implications. Adopting this approach helps ensure insightful risk assesments, robust decision-making and clarity in otherwise unexpected scenarios.