Author: Jesus Rodriguez

Source: Coindesk

Translation: Shaw Golden Finance

Insurance is one of the cornerstones of finance and an important pillar supporting all major markets, from commodities to credit. Since the 17th century, the prosperity of any vibrant financial ecosystem has relied on a robust insurance mechanism: market participants need quantifiable risk indicators before committing capital.

However, in the first wave of decentralized finance (DeFi)—lending, trading, derivatives—insurance has always been secondary, either existing in very rudimentary forms or not at all. As DeFi moves toward its next inflection point, embedding complex institutional-level insurance models will be crucial for unlocking significant capital and providing lasting resilience.

A Brief History of Risk and Insurance

The history of modern insurance is long. In the 16th century, Gerolamo Cardano's early writings on games of chance pioneered probabilistic thinking, describing uncertainty in mathematical terms (ultimately, his name was used in today's blockchain).

By the mid-17th century, the groundbreaking correspondence between Blaise Pascal and Pierre de Fermat laid the empirical foundations for probability theory, transforming randomness from mysticism into a quantifiable science.

By the 19th century, Carl Friedrich Gauss's formalization of the normal distribution enabled statisticians to systematically model deviations from expected values—a breakthrough tool for actuarial science.

In the early 20th century, Louis Bachelier's pioneering research on the random walk of asset prices heralded the rise of modern quantitative finance, influencing all aspects from options pricing to risk management.

Later in that century, Harry Markowitz's portfolio theory redefined diversification as a quantifiable process, providing a rigorous framework for balancing risk and return.

The Black-Scholes-Merton model further advanced the field by providing an easy-to-understand method for deriving implied volatility and pricing options (the cornerstone of modern derivatives markets).

In recent decades, innovators like Paul Embrechts and Philippe Artzner have enriched risk theory using copula statistical models and consistent risk measurement, enabling the systematic capture of extreme tail risks and systemic correlations.

Is DeFi Insurable?

Insurance requires four core prerequisites: diversified risk carriers, risk premiums exceeding capital costs, scalable capital pools, and quantifiable risk exposures. DeFi clearly presents quantifiable risks—protocol vulnerabilities, oracle manipulation, governance attacks—but insurability remains a challenge.

Early DeFi insurance schemes struggled due to limited actuarial techniques, untested capital structures, and high opportunity costs leading to exorbitant premiums.

Moreover, the rapid innovation cycle in DeFi creates a constantly evolving threat landscape: a vulnerability in one protocol rarely translates cleverly into another, and the speed of code changes outpaces traditional underwriters' ability to assess risk.

To overcome these obstacles, the next generation of insurance architecture must be able to dynamically adapt to changing risk conditions.

High-Cost Insurance Capital

At the core of any insurance structure is the cost of capital. DeFi insurance pools typically accept ETH, BTC, or stablecoins—assets that can generate on-chain yields through staking, lending, or providing liquidity. Therefore, insurers must offer returns above these native yields to attract underwriters, driving up premiums. This leads to a typical dilemma: high premiums deter protocol teams, while low capital costs undermine coverage capacity and reserves.

To break this deadlock, market architects must explore alternative sources of funding. Institutional investors—pension funds, endowments, hedge funds—have large capital pools and a long-term investment outlook. By designing insurance products that align with these investors' risk-return benchmarks (for example, offering clear upside returns in exchange for taking on first-loss risk through structured tranches), DeFi insurance architecture can achieve sustainable capital costs, balancing affordability and solvency.

The Law of Large Numbers Fails in DeFi

Jakob Bernoulli's law of large numbers is the foundation of traditional insurance: as the number of policies increases, the actual payout rate approaches the expected value, allowing for precise actuarial pricing. The mortality tables compiled by Edmond Halley and Abraham de Moivre exemplify this principle, converting demographic data into reliable premiums.

However, the emerging ecosystem of DeFi consists of a limited and often interrelated set of protocols. Catastrophic events, such as multi-protocol oracle manipulation, expose systemic dependencies that violate the independence assumption.

DeFi insurance cannot rely solely on transaction volume but must adopt a layered diversification strategy: reinsurance agreements across independent risk pools, capital allocation that prioritizes loss distribution, and parameter-triggered mechanisms for automatic insurance payouts based on on-chain indicators (such as price slippage thresholds, oracle deviation tolerances). Such architectures can approximate the smooth returns achieved by traditional insurance companies.

Challenges in Quantifying DeFi Risks

Quantitative risk modeling in DeFi is still in its formative stages. With only a few years of historical data and significant heterogeneity between smart contract platforms, inferring the risk of one protocol from another carries substantial uncertainty. Past attack events on Venus, Bancor, or Compound have provided useful forensic insights, but their predictive power is limited for new vulnerabilities in emerging protocols like Aave v3 or Uniswap v4.

Building a robust DeFi risk framework requires a mixed-methods approach: integrating on-chain analytics for real-time risk tracking, formal security verification of smart contract code, oracles for external event validation, and comprehensive stress testing against simulated attack vectors.

Machine learning models can enhance these methods—classifying protocols based on code patterns, trading behaviors, or governance structures—but must avoid overfitting sparse data. Collaborative risk alliances (where protocol teams and insurers share anonymous data on vulnerabilities and failure modes) can create a richer data foundation for next-generation models.

Towards an Institutional-Level DeFi Insurance Market

At its current scale, DeFi urgently needs reliable insurance solutions. Embedding mature, scalable insurance solutions can not only protect funds but also transform abstract risks (such as flash loan attacks, governance vulnerabilities, oracle failures) into quantifiable financial risk exposures. By adjusting product designs to align with institutional risk appetites, leveraging layered diversification investment strategies, and employing advanced quantitative risk models, a vibrant DeFi insurance market may unlock previously inaccessible capital pools.

Such an ecosystem is expected to provide deeper liquidity, enhance counterparty confidence, and attract a broader range of participants (from family offices to sovereign wealth funds), transforming DeFi from an experimental frontier into a cornerstone of global finance.

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