Uncovering Hidden Risks & Strategic Opportunities in a Shifting Global Landscape
Traditional risk models often focus on economic and financial indicators, missing broader systemic threats. We sought to develop a holistic, forward-looking risk assessment framework capable of detecting emerging global crises before they unfold.
bridge_ci utilized AI-driven risk analysis and machine learning models to detect early indicators of global systemic risks. By analyzing thousands of news articles, reports, and alternative data sources, we uncovered interconnections between health threats and economic vulnerabilities—months before the COVID-19 pandemic emerged.
Pandemic risk surfaced as a top concern in late 2019, well before COVID-19 disrupted global markets.
Early signals from alternative data sources suggested a growing link between health risks and economic downturns.
Machine learning models revealed overlooked warning signs, highlighting weaknesses in global preparedness.
Our analysis provided a critical early warning for potential pandemic-related disruptions. Financial institutions, multinational corporations, and policymakers who leveraged these insights were better positioned to prepare for economic shocks, adjust supply chains, and manage geopolitical risk in a volatile global landscape.
The analysis used topological data analysis to examine the geometric structure of store performance data, revealing distinct patterns and clusters that weren't visible through traditional statistical approaches. Store locations with similar characteristics were connected in a network visualization, with different metrics represented by color coding.
Policy advocacy is a high-stakes game where influence is often hidden beneath the surface. Organizations struggle to identify key stakeholders, understand their relationships, and predict their impact on policy outcomes.
Uncovering Hidden Risks & Strategic Opportunities in a Shifting Global Landscape
