In an operating environment shaped by predictably unpredictable geopolitical shocks, timely intelligence becomes a cost lever as well as a safety lever.
Our Strategy team have put together a topical business case to provide insights into the importance of geopolitical risk intelligence. Explore the insights below.
We estimate that the cumulative effects of airspace restrictions, operational disruption, and geopolitically driven volatility cost the global airline industry in the order of USD 5 – 12 billion annually.
This represents a small proportion of total industry revenues but a disproportionately large drag on profitability, reflecting the sector’s thin margins.
We estimate that 20-30% of these costs may be avoidable through the adoption of geopolitical risk‑intelligence tools that provide timely, predictive insight.
This estimate is based on an active global commercial fleet of approximately 31,000 aircraft, comprising around 25,000 narrow-body and 6,000 widebody aircraft. The breadth of the estimated range reflects the number of underlying variables and the resulting year‑to‑year variability in geopolitical developments.
To address this uncertainty, 10,000 simulations were conducted, with USD 5 billion representing the 25th percentile and USD 12 billion representing the 75th percentile of outcomes.
This USD 5 – 12 billion estimate is by nature conservative: it captures direct, first order operational disruption (e.g., rerouting and delays), but does not quantify broader knock-on effects - for example, when an aircraft is diverted or grounded and cannot operate its subsequent scheduled flights, as well as the resulting rerouting costs for passengers and crew.
In practice, geopolitical events can ripple through the global economic system and affect airlines well beyond those operating in and around the immediate conflict zone.
For example, a regional war can tighten jet fuel supply and raise prices for airlines globally. A scenario where this type of event causes a USD 50 per barrel increase in jet fuel, sustained for ~3 months, implies ~USD 30bn of incremental aviation industry fuel cost if purchased at spot.
Even allowing for hedging, such an event could still disrupt global supply chains for essential aircraft components, including semiconductors and avionics.
These second and third‑order impacts are difficult to measure reliably at an industry level. However, it is reasonable to expect that the exposure could greatly exceed the more readily quantifiable first‑order impacts.
Reactive approaches to geopolitical risk are increasingly insufficient.
Global air travel is expanding rapidly, with around 200,000 flights operating daily and this figure projected to roughly double by 2042. At the same time, geopolitical tensions and airspace restrictions are forcing more flights into fewer corridors, creating unprecedented operational challenges.
Risk is no longer confined to traditional conflict zones; disruptions originating in one region can quickly ripple across global networks. Rising traffic, shifting conflict dynamics, and constrained airspace are collectively increasing both risk exposure and operational costs for airlines.
Aviation leaders, including heads of security, operations, and insurers, emphasise that rising global instability is forcing airlines to shift from reactive approaches to proactive, real-time geopolitical monitoring.
For most of aviation history, airlines mainly relied on government‑led intelligence because it was the only authoritative, structured source of information about geopolitical threats, airspace restrictions, and security risks.
Regulators, embassies, and national security agencies controlled access to updates on conflict zones or safety concerns, and airlines depended on these official channels to know where it was safe to fly.
This system made sense in an era when governments held the best data; but it also meant airlines often received information that was slow, limited, or difficult to share across their operational teams.
The costs of disruption caused by geopolitics are multi-fold.
Geopolitical disruption is costly because it impacts multiple P&L lines simultaneously, through factors such as fuel burn, crew duty constraints, missed airport slots, diversions, fines, and insurance costs.
Commercial geopolitical risk intel solutions enable airlines to move proactively - rather than reactively.
Due to the shortcomings of relying on slow, inconsistent government intelligence, and the heavy operational burden of manually interpreting raw information for aviation, airlines increasingly look to commercial risk‑intelligence platforms.
Best‑in‑class risk intelligence means airlines get fast, verified, aviation‑specific insights about any threat that could affect a flight, crew or airport. It goes far beyond news scraping or waiting for late government notices. Instead, solutions exist that integrate data from multiple sources, including news platforms, social media, government notices, satellite and thermal imagery, aircraft movement data and filters it into accurate, unbiased, real-time and even predictive alerts.
By crosschecking data inputs and filtering out unreliable data, these platforms help ensure that risk assessments rely on objective and verified information rather than isolated or subjective observations. Airlines value this because decisions on overflight, routing, and airport safety and security must often be made within minutes, not hours, and are further strengthened by predictive alerts that enable contingency planning.
It also means the intelligence is interpreted, not just delivered raw. Leading platforms show what threats mean for actual flight paths, altitudes, aircraft types and crew safety. Historical trends, predictive analysis and easy‑to‑use dashboards help airlines assess whether risks are rising, falling or stable.
Ultimately, best‑in‑class intelligence supports safe but optimised operations; avoiding unnecessary costs while avoiding genuine hazards. It enables airlines to implement timely mitigation measures as risk rises - neither too early nor too late - while also supporting dynamic route reopening once risk returns to acceptable levels, with decisions clearly justified across the organization, including to crew and passengers.
Industry adoption remains limited, with most airlines operating below best practice.
Only an estimated 15–20% of airlines, typically larger and more safety‑mature carriers, have adopted truly best‑in‑class, daily risk‑intelligence tools. These airlines treat risk intelligence as essential infrastructure rather than a “nice to have,” and expect immediate alerts, deep analysis, forecasting, and aviation‑specific insight.
By contrast, the majority of airlines continue to rely on government advisories, media reporting, manual checks, or generic intelligence tools not designed for aviation. Even among larger carriers, capability is often fragmented or incomplete. While adoption is accelerating in response to recent global events, the industry overall remains in the early‑to‑middle stages of maturity.
The market is somewhat under‑served, with few vendors capable of offering a comprehensive, real‑time risk picture.
Figure: Capability comparison of aviation intelligence and risk‑monitoring providers, ordered by completeness of coverage on the evaluation categories.
Perceived intangibility of ROI is an adoption hurdle for some.
Many airlines struggle to justify investment because the financial benefits of risk-intelligence tools are not easily expressed in a simple, measurable way.
Many airlines we’ve spoken to say a major barrier to adopting commercial risk-intelligence solutions is that the ROI feels difficult to articulate or quantify.
While the operational value is often clear to them – better decisions, faster intelligence, and improved safety and security – the financial return is harder to convert into a simple number. Much of the benefit comes from “losses avoided”, which procurement teams struggle to treat as tangible.
Combined with tight budgets and competing priorities, this leads many airlines to continue relying on slower government intelligence or manual assessment processes rather than investing in commercial platforms.
This challenge is reinforced by organisational dynamics. Security and operational teams are, for valid reasons, not commercially driven, which can make it difficult to build a strong business case.
In hindsight, better intelligence would have changed some past decisions and avoided costly outcomes, but acknowledging this can be difficult, particularly where it implies that earlier judgments had material financial consequences.
As a result, there is often a tendency to protect the status quo. In these cases, airlines may rely on external justification, such as alignment with government guidance or peer decisions, rather than taking ownership of different, independent judgements enabled by next-generation tools.
While long-haul and high-exposure airlines see large, measurable returns, even airlines operating in supposedly low-risk regions are recognising that unexpected events can quickly turn risk intelligence into a critical operational asset.
Airlines also highlight that ROI varies dramatically depending on route network and exposure profile. Global carriers flying long-haul over conflict-affected regions see very large, measurable gains: airspace reopening, fuel savings, avoided detours, and reduced crew-time impacts. Some report that a single routing decision can outweigh the annual platform cost.
Many airlines operating in what they believe are stable environments don’t immediately see the benefit; yet that stability is increasingly fragile.
Recent events across Europe show how quickly conditions can change, whether through geopolitical shocks or sudden, highly disruptive drone incursions.
In late 2025, for example, drone sightings shut down airports across Belgium, Denmark, Germany, and the Netherlands, including a complete suspension of flights at Eindhoven Airport, showing that even ‘low-risk’ regions can face major operational disruption with no warning.
Even adopters acknowledge that ROI fluctuates widely based on routes, exposure, disruptions, and seasonality, leaving non-adopters unsure how to quantify value.
Many airlines using these platforms generally describe payback in weeks rather than months, with most recovering the investment well within a single operational season. Others view payback as a longer-term accumulation of safety, efficiency, and compliance benefits. In both cases, many airlines are still making ROI judgments based on gut feel and intuition.
For those that already use these tools, they have become core infrastructure, making financial justification far easier than in the past. However, for airlines yet to adopt them, the perceived low measurability of returns remains a key reason for hesitation.
Scenario modelling shows that adopters of these solutions can expect a positive ROI.
A probabilistic approach to understanding ROI is needed because the disruptions driven by geopolitical tensions are highly variable by nature.
Because ROI is highly variable across fleets, routes, aircraft mix, and geopolitical exposure, many airlines struggle to form a confident financial case for adopting risk-intelligence tools. To address this uncertainty, we’ve developed a structured way to quantify ROI using a Monte Carlo simulation.
This approach reflects the reality that disruption costs depend on wide-ranging factors: aircraft types, daily flight volumes, percentage of routes exposed to geopolitical tension, the likelihood of diversions, typical delay minutes, and the rare but costly grounding of aircraft.
Monte Carlo is well-suited here because it helps model situations where uncertainty genuinely matters and simple averages hide meaningful variation.
In practice, the model runs ten-thousand simulations for an archetypal 100-aircraft airline, randomising inputs such as fleet mix, daily flight volume, disruption rates, diversion probabilities, additional flying minutes and the occasional multi-day grounding of aircraft.
Each run produces a different annual disruption cost, building a realistic distribution of low, median and high financial outcomes. A conservative mitigation rate (e.g., 10-20%) is then applied to estimate the portion of these costs that risk-intelligence tools could reasonably avoid.
The analysis also incorporates the per-aircraft insurance discount realised by adopting a geopolitical risk management solution.
Comparing those savings to the total solution cost (e.g., ~USD 10-12k per tail) produces a range of plausible ROI results, highlighting typical, downside and upside scenarios.
A typical airline can expect to recover the cost of a one‑year subscription to a geopolitical risk‑intelligence solution in around 6–8 weeks.
We modelled 10,000 scenarios to understand the payback period for adopting a geopolitical risk-intelligence solution.
Each simulation varied core drivers such as fleet mix, flight volumes, disruption rates, diversion probabilities and grounding events, producing a wide distribution of possible financial outcomes.
The results show that while payback periods naturally differ across operating profiles, the distribution clusters tightly enough to give a clear picture of the likely return.
Across all 10,000 simulations, the median scenario achieved payback in around 7 weeks, reflecting the cumulative effect of avoided disruption, shorter routings and faster operational decisions.
Early-benefit cases demonstrate even faster recovery, with 25% of scenarios paying back within 5 weeks.
Even under more conservative conditions; where disruption is less frequent, rerouting benefits are smaller, and grounding events are rare; 95% of cases reached payback within 24 weeks.
This indicates that, although individual airlines will experience different levels of impact, the underlying economics remain robust across a broad range of real-world conditions.
Typical scenarios have been modelled, but real-world benefits can be even larger.
Black Swan events aren’t modelled; even though they can happen, they are rare and massively distorting.
The model reflects expected costs and benefits under normal operating conditions, based on disruption patterns that occur frequently enough to measure.
It does not attempt to predict extreme, ultra-rare events that fundamentally sit outside normal risk assumptions and would distort everyday decision-making.
In aviation, a true black swan is less about the existence of conflict risk itself, much of which is well precedented, and more about novelty or unprecedented scale, such as the use of aircraft as weapons on 9/11, or state-level actions that far exceed historical experience.
Black Swan events
Black swan events are shocks so extreme, either in nature or scale, that no normal operational or commercial model can meaningfully account for them.
By contrast, damage to aircraft or airport infrastructure in active conflict zones, while severe, follows a well-established risk pattern and should be explicitly incorporated into risk assessments and forward-looking operational planning rather than treated as statistical outliers.
Opportunity-cost gains and second-order disruption impacts are not explicitly modelled, even though they can be material for some operators.
The model captures direct efficiency benefits such as shorter flight times and reduced fuel burn from more optimal routings. It does not, however, reflect the wider operational value unlocked when improved geopolitical risk intelligence gives airlines the confidence to move beyond blanket guidance or long-standing avoidance habits.
Examples include reopening routings over parts of Africa, such as Libyan airspace, or removing overly conservative detours in South America, such as around Venezuelan airspace. The upside for these airlines can extend beyond fuel savings to improved aircraft utilisation and network efficiency.
The model also does not capture knock-on disruption costs when adverse events occur, such as passenger reaccommodation, crew displacement, or the inability to operate subsequent sectors following a diversion.
As these effects are highly operator- and network-specific, they sit outside the core ROI calculations.
Small-fleet effects aren’t fully modelled because the 100-aircraft baseline is designed for scale, and percentage impacts can be disproportionately higher for smaller operators.
The model is built around a 100-aircraft scenario, simply to give a clear, scalable picture of the commercial impact of geopolitical disruption. However, for many regional and mid-sized operators, losing even a single aircraft or experiencing a multi-day routing disruption can represent a much bigger percentage hit to their overall operation than it would for a 100-tail carrier.
That means the risk exposure is proportionally higher; and so the ROI case for predictive, real-time risk intelligence becomes even more compelling.
In an operating environment shaped by predictably unpredictable geopolitical shocks, timely intelligence becomes a cost lever as well as a safety lever
Priority actions for airlines:
KPMG's independent assessment of the value of geopolitical risk intelligence is informed by multiple sources, with thanks to Osprey Flight Solutions for specific data contributions and support with production costs.
Implementing geopolitical risk intelligence solutions can deliver rapid payback for airlines, with a typical payback period of 6-8 weeks.
Connect with us today to explore how our strategic services can support your organisation on this journey to achieve results and cost-efficiency.
