Financial institutions are increasingly recognising the importance of the specific geographic location of company assets. From climate change and natural catastrophes to supply chain disruptions and geopolitical conflicts, many risk drivers are inherently spatial. Traditional risk data – often aggregated at the firm or sector level – can obscure local vulnerabilities.
Regulators and frameworks worldwide, such as the Taskforce on Climate-related Financial Disclosures (TCFD), emphasise the importance of the assessment of physical climate risks within a firm’s operations, which inherently demands location-specific analysis.
Accurate, validated asset location data enables a clearer understanding of multiple financial risks including climate risk, nature risk, credit and counterparty risk, operational and supply chain disruptions, and even geopolitical exposures – ultimately supporting better decision-making.
Why location matters
Traditional environmental, social, and governance (ESG) data and risk metrics are often reported at the company or regional level, averaging out the nuances of individual sites. This can lead to blind spots. The UK Centre for Greening Finance & Investment (CGFI) notes that a company’s environmental impacts and dependencies are “inherently location and context specific”. Without asset-level location intelligence, risk analyses may miss critical hotspots of risk.
Firm-wide averages dilute local extremes and priority locations. An oil and gas company, for instance, might disclose a general climate risk rating or an aggregate biodiversity impact, but that fails to distinguish a refinery built in a coastal hurricane zone from one located inland. Similarly, the risk profile of a mine in a fragile tropical ecosystem, versus one in a barren desert, is incomparable.
Emerging research shows that incorporating high-resolution spatial information can dramatically improve risk analysis. Understanding the precise location of a company’s operations enables analysts to integrate asset-level exposure with specific environmental conditions, resulting in a deeper understanding of how assets could be impacted by factors such as natural disasters and climate change.
Crucially, spatial context reveals hidden risks that would be invisible in aggregate data. In nature risk analysis, this is framed as ‘double materiality’ – how nature impacts the company and how the company impacts nature – both sides being fundamentally location-dependent. For example, a large agribusiness or data centre might appear low-risk in a broad ESG scorecard until, for example, geospatial analysis shows its plantations or operations overlap with endangered species habitat or water-scarce basins.
Pinpointing which assets sit in harm’s way – whether environmental sensitivity or physical hazard – are rarely attainable through coarse data or voluntary corporate reporting alone. These granular details are necessary to provide a holistic and accurate understanding of a company’s overall assets and operations and its collective impact and dependency on nature.
Diversifying risk
Location precision is paramount for physical climate risk assessment. Climate hazards like floods, hurricanes, wildfires, heatwaves and sea-level rise are unevenly distributed across geographies. Even within a single country, one region may face frequent flooding while another suffers drought. An organisation can only gauge its true exposure to climate extremes by mapping which assets lie in harm’s way. Integrating asset coordinates with climate data yields more accurate loss projections and resilience plans.
In practice, mapping loan exposures to specific locations revealed that a large share of one country’s banking assets were tied to operations in a single high-risk region – a concentration risk that would have been obscured without geospatial mapping. Identifying this risk meant that necessary action was taken to diversify the risk.
Lessons from the insurance industry
Financial firms are now borrowing tools long used by the insurance industry. Insurers have traditionally priced property coverage by evaluating location-specific catastrophe models – for example, using a building’s coordinates to estimate hurricane wind damage risk. Banks and asset managers are starting to do the same for climate risk, linking each asset or collateral to local climate hazard data.
This allows for a bottom-up aggregation of risk, enabling more precise differentiation of risk levels within region. For example, identifying a Southeast Asia facility sitting on high ground with a flood defence levee as low risk, versus another facility within the region sitting along an unprotected riverbank as high risk. Such differentiation is only possible with precise geospatial intelligence.
Precise asset location data is the linchpin for understanding physical climate risk. It turns abstract projections of climate change into concrete insights about which investments are exposed, enabling investors to make informed decisions.
Conclusion
Asset location precision has moved from a niche concern to a foundational element of financial risk analysis. High-resolution geospatial data transforms how banks, investors and insurers understand their exposure, offering a lens to see risks that were previously hidden. This empowers a shift from reacting to surprises to proactively mapping vulnerabilities and preparing for them.
High-resolution asset location data represents a powerful innovation at the nexus of finance, technology and science. It is more than a technical nicety; it is a strategic imperative for risk-aware financial institutions. By integrating granular location intelligence into risk frameworks, institutions can drive more resilient allocation of capital throughout a period where growth is key.
