Description: This paper examines the potential impact of increasing negative commentary by social media users in multiple forums, which can erode the “reputation capital” of a utility. It also offers an approach for successfully addressing that risk and integrating social media risk management practices into existing utility enterprise risk management (ERM) programs.

Description: Adaptive Risk Management harnesses social intelligence to identify emerging risks. We have entered an age of disruption where exponential technologies are transforming every industry. The ability to pivot in a rapidly evolving ecosystem is essential. Centralized “predict and control” paradigms will increasingly fail as complex interactions drive phase transitions. Successful organizations will harness social intelligence to “sense and respond” to giant waves of change. Maps amplify intelligence to help us navigate challenging terrain. Maps play a central role in all command centres, enabling rapid risk identification with a shared visual language. Interactive financial network maps allow analysts to explore and diagnose systemic fragilities, and mitigate critical vulnerabilities. Dynamic financial maps can also build visual intuition to detect escalating risk patterns. After observing early warning signals, analysts can apply network analytics to estimate likely second order effects. This allows organizations to proactively manage emerging risk before tipping points are crossed.

Description: Adaptive Risk Management harnesses social intelligence to identify emerging risks. We have entered an age of disruption where exponential technologies are transforming every industry. The ability to pivot in a rapidly evolving ecosystem is essential. Centralized “predict and control” paradigms will increasingly fail as complex interactions drive phase transitions. Successful organizations will harness social intelligence to “sense and respond” to giant waves of change. Maps amplify intelligence to help us navigate challenging terrain. Maps play a central role in all command centres, enabling rapid risk identification with a shared visual language. Interactive financial network maps allow analysts to explore and diagnose systemic fragilities, and mitigate critical vulnerabilities. Dynamic financial maps can also build visual intuition to detect escalating risk patterns. After observing early warning signals, analysts can apply network analytics to estimate likely second order effects. This allows organizations to proactively manage emerging risk before tipping points are crossed.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.