Civil unrest events, driven by factors like inequality and political polarization, have surged in both frequency and intensity in recent years, resulting in a 3000% increase in claims under so-called Strikes, Riots, and Civil Commotion (SRCC) coverages[1]. This sharp rise has pushed the insurance industry to refine their risk assessments to better anticipate losses. However, traditional risk models often fall short of capturing the evolving nature of civil unrest. This article explores how advancements in analytics can improve the prediction of riots and civil unrest, enhancing risk management strategies to reduce financial impacts.
While riots may sometimes seem spontaneous, research suggests they are often influenced by underlying factors. Key predictive indicators include:
Economic inequality
Countries with stark income inequality tend to experience more civil unrest. The two costliest episodes since 2020 occurred in the first and third most unequal countries by Gini index[2]: South Africa suffered $1.7 billion in insured losses during the 2021 riots[3], while Colombia faced claims well into the hundreds of millions of dollars in the same year.
As inequality rises, the likelihood of civil unrest may increase[4].
Political representation gaps
Government distrust and political disillusionment drive growing grievances and unrest. The 2025 Edelman Trust Barometer shows government is distrusted in 17 of 28 countries, and over half of young people support hostile activism, including violence. This disconnect increases the likelihood of protests and extreme actions[5].
Digital connectivity
Urban centers with high social media penetration see faster protest mobilization, amplifying unrest. The 2019 Chilean riots illustrate this, as calls to action spread rapidly online, turning a subway fare protest into nationwide demonstrations[6].
According to the Carnegie Endowment for International Peace (CEIP), over 150 countries have experienced significant civil unrest since 2017, highlighting global trends of social and political unrest across various political systems and cultures, thus providing a rich dataset for identifying patterns[7]. These observed patterns suggest that insurers could enhance risk assessment models by considering factors beyond traditional geographic boundaries.
While each outbreak of civil unrest is unique, identifiable patterns can often be observed. By analyzing historical incidents, certain high-risk conditions can be identified and protest epicenters predicted.
For instance, the 2020-2021 Black Lives Matter protests in the U.S., sparked by George Floyd’s killing, were centered in urban hubs and culturally significant locations[8]. In Portland, protests focused on the Mark O. Hatfield Federal Courthouse, while in Washington D.C., they concentrated around Lafayette Square[9]. These spaces were strategically chosen to draw media attention and highlight issues like police brutality and systemic racism.
The causes of civil unrest often influence its geographic concentration:
Although unrest may initially spread unpredictably, its geographic reach is often influenced by law enforcement responses. The effectiveness of security forces can play a significant role in limiting the escalation of unrest, while disorganized or delayed responses could potentially allow it to spread. Protesters’ willingness to face arrest or injury also influences how events unfold.
State of the art SRCC models, such as the Synthetik SRCC Quantum Tool, use spatial risk indicators, or ‘target points,’ to classify locations based on their role in civil unrest, including government buildings, financial institutions, or religious sites. This spatial approach allows insurers to assess SRCC risks with greater precision and nuance[10].
For example, according to Synthetik in 2020, only 3% of Minneapolis was classified as high-risk for an SRCC event. The model identified key target points across economic, religious, and political categories, allowing for a more precise mapping of risk distribution.
374 SRCC Target Points identified in Minneapolis (blue – economic, orange – religious, green – political)
Sources: Screenshots from Synthetik SRCC Quantum Tool
Understanding riot contagion — how unrest spreads — is crucial to modeling. Traditional models emphasized geographic proximity, assuming unrest radiates outward from an epicenter. However, digital technology has transformed these dynamics. Social media platforms like X, Facebook, and encrypted apps such as WhatsApp and Telegram enable real-time coordination and rapid mobilization, allowing protests to ignite simultaneously in multiple locations rather than spreading gradually from a central point.
The 2024 anti-government protests in Kenya demonstrated this shift. Activists used social media to organize demonstrations against tax increases and corruption, with the hashtag #RejectFinanceBill2024 gaining significant traction on social media platforms[11]. When the government restricted internet access, protesters used VPNs and encrypted apps to maintain communication and coordinate their actions across the country[12]. Live-streamed police responses amplified the protests, drawing global attention. This case highlights how digital tools enable real-time, decentralized mobilization, transforming civil unrest dynamics. Rather than spreading outward in concentric rings, modern SRCC models recognize protests follow linear pathways — shaped by roads, symbolic targets, and law enforcement presence. Insurers must adapt by incorporating these patterns into predictive models, replacing outdated geographic assumptions.
Civil unrest risks are set to grow due to global challenges like climate change, resource constraints, and economic instability. Widening wealth gaps and declining institutional trust are likely to fuel future demonstrations and riots, pushing unrest beyond traditional flashpoints.
To address this, it is important that risk management professionals adopt proactive risk models that integrate historical data with social, political, and economic indicators. Key factors for future models include:
Managing civil unrest risk requires a dynamic strategy. Unlike natural disasters, these events are influenced by ever-changing human behavior and law enforcement responses. To tackle this, any Probable Maximum Loss (PML) model must involve a combination of varying riot severities, durations, and intervention strategies.
According to the Synthetik Quantum SRCC model, for example, a 2020 Minneapolis study simulated over 1,000 civil unrest scenarios, estimating a maximum loss of $700 million affecting 1,773 properties[13]. The actual losses from the George Floyd protests were around $500 million, impacting just over 1,000 properties — a strong validation of the model's accuracy, and suggesting that losses may have been even higher under different circumstances.
As political volatility intensifies, modern modeling approaches — incorporating demographic shifts, digital contagion dynamics, and spatial risk indicators — will be essential for managing financial impacts. To navigate rising civil unrest risks, risk managers must move beyond reactive strategies and embrace sophisticated predictive models. Doing so empowers loss mitigation strategies and enables insurers and buyers to come to the negotiation table from a position of strength.
