With less than five years until 2030, the Sustainable Development Goals (SDGs) are slipping further out of reach. Trillions of dollars in unmet financing needs remain, while some development banks and private investors continue to hold back from emerging and developing economies (EMDEs), citing “sovereign risk.”
The problem is not just the data — it is the methodology. National-level ratings were never designed for today’s investment landscape. They compress diverse realities into a single score, collapsing institutional, geographic, and sectoral variation into a blunt measure. For investors in Washington or London, the risk of “Kenya” or “Brazil” appears uniform. Yet in practice, one province may have resilient infrastructure and credible courts while another struggles with instability. Current methodologies obscure these differences, creating a risk premium that blocks projects not because they are unfinanceable, but because the lens itself is too wide.
Green Bonds in Latin America Foucsing on Subnational Exposures
In some instances, we have seen what happens when risk is measured differently. In Latin America, several municipal green bonds for water and infrastructure projects succeeded only because subnational development banks built bespoke assessments. These methodologies revealed that certain cities had stronger fundamentals than the sovereign profile suggested: credible repayment capacity, transparent budgeting, and stable governance. Under national ratings, these municipalities looked indistinguishable from their central governments, whose weaker fiscal records cast a long shadow.
Once the methodology shifted, the data told a different story. Investors could finally see that the municipal risk profile was healthier than the sovereign’s, and capital began to flow. These projects didn’t succeed because risk vanished — they succeeded because risk was framed at the right level.
The Benefits of Sub-sovereign Analysis
That distinction points to a broader truth. Sub-sovereign analysis does more than sharpen nuance: it highlights the methodological blind spots of sovereign risk models. Reducing risk means showing that a local government or project is objectively safer than its sovereign score suggests. Reducing uncertainty means filling in the gaps where information was once absent. Investors often conflate “unknown” with “unsafe.” When they cannot see what lies below the sovereign level, they assume the worst. By making hidden signals visible — from court performance to budget flows — AI lowers that uncertainty premium and gives risk officers the opportunity to implement mitigation and transfer strategies.
Historically, this kind of assessment was rare and resource-intensive. It required custom data collection, costly modeling, and extensive handholding. For most EMDEs, the sovereign shadow still dominates: if the country is rated high risk, the projects inside it are invisible. Billions in financing remain trapped behind blunt methodologies.
This is where artificial intelligence changes the equation. AI doesn’t just add more data points to existing sovereign models; it reframes what risk itself means. The unit of analysis shifts from nation to project and sub-sovereign jurisdiction. Suddenly, the governance quality of a county court, the resilience of a local grid, or the credibility of a municipal budget can be measured and monitored in real time.
Satellite imagery can flag where land disputes are resolved or escalating. Natural language processing can scan local court rulings. Machine learning can integrate budget flows, NGO reports, and social signals into dynamic risk maps. What was once anecdotal and invisible becomes systemic and measurable.
From National SDG Risk to Local SDG Risk
Seen through this lens, the SDG financing gap looks less like a story of capital scarcity and more like a story of methodological misperception. Trillions in global capital sit idle not because projects are inherently unfinanceable, but because the analytical frame is misaligned with reality. AI offers a lens-changer: a way to redesign methodologies so that viable projects are recognized rather than written off as lost causes.
The payoff could be profound. Instead of writing off entire nations, development banks could channel financing to subnational jurisdictions that demonstrate resilience. Private investors could price risk with greater precision, lowering the cost of capital for communities that have long paid for their national neighbors’ instability. What once required years of bespoke due diligence could now be generated instantly at scale.
The implications are human as much as financial. A blunt sovereign rating doesn’t just distort markets; it blocks a hospital in a county with strong governance or a renewable project in a city with resilient infrastructure. Updating methodologies is not merely an efficiency upgrade — it is a bridge between capital and communities excluded by misperception.
AI does not guarantee the SDGs will be met, but by modernizing methodologies and scaling sub-sovereign assessments it reduces both risk and uncertainty — enabling more capital to move toward SDG-aligned projects than blunt national indicators ever allowed