Wall Street is increasingly exploring war-risk modeling as part of broader market-risk toolkits, as financial firms look for earlier signals on events that may affect energy prices, credit markets, insurance costs, shipping routes and supply chains.
The shift follows a period in which conflict risk has appeared harder for traditional models to capture through historical averages alone. Public reporting said financial firms are gaining access to catastrophe-modeling techniques originally built for natural disasters and now being adapted to estimate the likelihood of military conflict.
War risk may move through markets quickly. Oil, freight rates, defense shares, insurance premiums, sovereign debt and currency markets can all face pressure when conflict raises uncertainty around supply chains or regional stability. For Wall Street, the central question is how firms may use data tools to quantify geopolitical stress before risks are fully reflected in market prices.
A New Risk Layer for Wall Street
Verisk Maplecroft has introduced a Predictive War Index designed to estimate the likelihood of war occurring in a country over a 12-month period. According to public reporting, the model was released to clients in late May and uses a machine-learning algorithm trained on political, economic and social datasets from 1995 to 2022.
The company said back-testing showed that, had the model been available in early January, it would have indicated a 66 percent probability of war breaking out in Iran about six weeks later. That figure does not mean a model can predict future events with certainty. It suggests how firms are attempting to convert warning signs into probability-based risk signals.
Verisk also has a Geopolitical Relations Index that tracks tension between country pairs. Public descriptions say the tool considers factors such as past clashes, geographic proximity and the ability to project power. For financial firms, that type of model may help translate conflict risk into exposure reviews for shipping, energy and insurance.
Why Conflict Risk Is Moving Into Models
Since 2008, the number of countries engaged in external conflicts has nearly doubled to just over 100, according to reporting citing the Institute for Economics and Peace. The economic impact of violence has been estimated at nearly $22 trillion, equal to more than 10 percent of global gross domestic product.
Those figures may create a practical challenge for banks, insurers and asset managers. Standard risk systems often work more clearly when market shocks resemble earlier episodes. Conflict risk can be different. A maritime disruption, sanctions shift, infrastructure strike or border escalation may appear suddenly, then place pressure on physical commodities, credit spreads and corporate cash flow at the same time.
That is why catastrophe-modeling firms are gaining attention in the discussion. These firms have long estimated how storms, earthquakes and other physical risks may affect property losses. Applying similar scenario methods to conflict does not remove uncertainty, but it may give financial teams a more structured way to test exposures before losses appear in market prices.
AI Tools Meet Market Limits
The use of AI in conflict modeling remains a tool, not a definitive forecast. Machine-learning systems depend on data quality, model assumptions and measurable signals. A model trained on data through 2022 may not capture newer patterns, while sudden leadership decisions, miscommunication or accidental escalation can still fall outside expected ranges.
BlackRock’s Geopolitical Risk Indicator offers another example of how market firms are using machine learning in this area. Its methodology says the indicator tracks market attention to geopolitical risks by reviewing brokerage reports and financial news, then using language models to classify relevance and sentiment. BlackRock also describes scenario work for defined market-risk conditions.
Separately, researchers are testing open-source intelligence tools that track geopolitical narratives in real time. A recent arXiv paper introduced CausalAlpha, a framework that builds a high-frequency geopolitical risk index from Telegram channels and studies links with commodity prices, equity indexes and credit instruments. The paper found some links between conflict coverage and energy-sector equity returns, while noting that daily transmission to broader macro prices appeared statistically weak.
What Wall Street Market Teams Are Watching
The practical use case may not be a single prediction. It is more likely to be a dashboard of probabilities, relationships and scenarios reviewed alongside market data. For Wall Street risk teams, the focus may include oil transit routes, LNG supply, defense spending, insurance cover, port activity, currency stress and credit quality of firms exposed to unstable regions.
Insurers may be among the early users because conflict can affect underwriting, claims and reinsurance pricing. A Verisk Maplecroft report said geopolitical volatility had climbed nine places to become the fifth future risk in Aon’s Global Risk Management Survey in late 2025. The same report said insurers are using scenario analysis to assess how business lines may change under different paths.
For banks and corporate finance teams, the tools may also support stress tests. A lender with exposure to shipping, energy infrastructure or emerging-market debt may use conflict-risk scores to review collateral, covenants and liquidity buffers. A multinational company may use similar signals to examine supply contracts, regional revenue concentration and insurance coverage.
The appeal for Wall Street is speed and structure. If conflict risk can be updated more frequently than traditional country-risk reports, market participants may gain another way to assess where pressure could be building. That does not make the models certain. It may make the risk conversation more data driven.
One possible development is cultural as much as technical. War risk, once treated mainly as a qualitative input from regional specialists, is increasingly being pulled into the quantitative workflows used for rates, credit, commodities and volatility. For U.S. markets, that may influence how firms monitor shocks that begin overseas but reach domestic portfolios through oil prices, inflation expectations, sector earnings and funding costs.
