Prof. Hernan Huwyler, MBA CPA

Artificial intelligence techniques can be used to detect changes in the risk context, particularly in scenarios where unusual activities are present. Anomaly detection algorithms can identify deviations from expected behavior, enabling risk managers to respond promptly to emerging risks. The use of predictive risk models and AI in risk management enables organizations to shift from a reactive to a proactive approach, anticipating and mitigating risks before they materialize.

Risk managers can use advanced predictive risk models and data analytics to identify correlations between risk losses and contributing factors. For example, machine learning clustering techniques can be employed to classify and segment potential causes of losses, enabling risk managers to identify and assess their causal relationships. By analyzing patterns of near-misses and losses using predictive modeling, risk managers can gain valuable insights to inform data-driven decisions and proactively engage risk owners with actionable intelligence.

When external conditions change and affect the effectiveness of controls, outsourcing, or insurance coverage, risk exposure must be reassessed. In this situation, risk managers can conduct a new scenario analysis to adjust estimated impacts, gather updated risk data, and identify new losses or external factors. Metrics can be used to track changes in vulnerabilities and third-party risks. AI-driven analytics can support a proactive approach by detecting shifts in external threats, such as monitoring sentiment and unusual activity. Crisis protocols and real-time scenario analysis are valuable tools for managing external shocks.

Growth initiatives introduce new risks that can make stakeholders uneasy. These risks may include market volatility, financial uncertainty, and operational challenges. Risk managers start with clear goals and assumptions that align with the business development plans. This goal-oriented approach helps maintain focus to assess risks. They assessethe impact of market volatility on financial projections, revenue forecasts, cost estimates, and return on investment. Conducting a scenario analysis shows how different outcomes might affect the project, helping stakeholders understand the range of possible results and the measures in place to manage them.

To effectively counteract resistance to risk mitigation actions such as procuring insurance policies, discontinuing engagement with unreliable third-party services or technologies, or instituting necessary controls, you need to demonstrate how potential incidents could affect business objectives, profitability, customer satisfaction, and compliance obligations. Much of the resistance in risk management comes from a misalignment with business goals. Quantifying risks to create cost-benefit and ROI analyses helps communicate the importance of mitigation. You can use real-world case studies from organizations facing similar threats to show the potential impact. Suggest incremental changes through pilot programs or phased implementations.

Risk managers can involve stakeholders early and often in business planning to prevent conflicting priorities among decision-makers. They can facilitate open discussions about the certainty of assumptions and gather different perspectives and concerns. By using quantitative and predictive risk models, they can compare risk exposure to the organization's ability to handle risk before discussions and potential conflicts escalate. Risk managers promote transparency and active listening to ensure everyone’s views are heard, helping to identify the root causes of conflicting priorities. Presenting objective evidence can bridge gaps between differing opinions and align stakeholders on the most critical risks.