Adverse selection occurs when high-risk actors are disproportionately drawn to purchase insurance, skewing the risk pool.

• Users with known vulnerabilities in a protocol are more likely to buy coverage.
• This leads to higher-than-expected claims, increasing costs for all participants.
• It matters because it can destabilize a protocol's capital pool and lead to insolvency if not priced correctly.

Post-coverage moral hazard refers to the incentive for a protected party to act recklessly or neglect risk mitigation.

• A protocol team with coverage may delay critical security patches.
• A liquidity provider might engage in riskier yield farming strategies.
• This matters as it increases the likelihood and severity of claims, harming the collective capital backers.

The principal-agent problem arises when a decision-maker (agent) does not bear the full consequences of their actions, which are instead borne by capital providers (principals).

• A protocol's developers (agents) make technical choices, while cover purchasers and stakers (principals) hold the financial risk.
• Misaligned incentives can lead to suboptimal security practices.
• This is central to DeFi insurance, requiring mechanisms like staking slashing to align interests.

Asymmetric information exists when one party in a transaction possesses more or better information than the other, leading to market inefficiency.

• A protocol team has superior knowledge of its codebase's weaknesses compared to coverage buyers or stakers.
• This can enable hidden risks or fraudulent claims.
• It matters because it underpins both adverse selection and moral hazard, necessitating transparency and audits.

Risk pooling is the foundational mechanism of insurance, where many participants contribute capital to cover the losses of a few.

• Effective pooling requires a large, uncorrelated set of risks to ensure sustainability.
• In DeFi, correlation risk (e.g., a systemic smart contract bug) can threaten the entire pool.
• This matters because poor diversification exacerbates moral hazard by concentrating protocol-specific risks.

Incentive design involves structuring economic rewards and penalties to mitigate moral hazard and align stakeholder interests.

• Examples include requiring coverage buyers to co-insure (deductibles) and slashing staker rewards for poor claims assessment.
• Well-designed incentives ensure that risk-takers have "skin in the game."
• This matters as it is the primary tool for creating sustainable and trustless insurance protocols.

The Claims Assessment process relies on member voting, creating a potential conflict of interest for voters who also hold coverage. This system was tested during the 2021 Cover Protocol exploit, where a contentious claim highlighted challenges in adjudicating complex smart contract failures and the moral hazard of voters minimizing payouts to protect the mutual's capital.

• Staked MCR (Minimum Capital Requirement) backs claims, aligning capital providers with protocol solvency.
• Claim Assessors vote on validity, with economic incentives to vote with the majority.
• The process demonstrates the tension between claimant rights and the collective interest of capital providers.

This model uses segregated Capital Pools for different risk categories, which mitigates cross-contamination but introduces a capital allocation moral hazard. Providers may favor over-collateralized, low-risk pools, starving coverage for novel or complex protocols. The 2022 UST depeg event showed how correlated failures can stress-test pool segregation and risk-pricing models.

• Risk Segregation prevents a single event from draining all capital.
• Capital Efficiency is challenged by the need to overallocate to uncertain risks.
• The model highlights the trade-off between safety and coverage availability for emerging DeFi sectors.

Cross-chain coverage expands the addressable market but introduces unique moral hazards around solvency monitoring and claims facilitation across fragmented networks. The protocol's design, which uses a main chain for underwriting and capital management, was scrutinized after the Multichain bridge collapse, revealing challenges in assessing and responding to cross-chain insolvency events in a timely manner.

• Portfolio Diversification across chains reduces correlated risk from a single network.
• Claims Processing latency and cost increase with cross-chain messaging.
• This case underscores the operational risks and informational asymmetries in multi-chain insurance.

Sherlock's model ties coverage to a technical audit, shifting risk assessment from financial actuaries to code reviewers. This creates a moral hazard where auditors, paid upfront by projects, may face conflicts between rigorous review and client satisfaction. A dispute over a 2023 exploit in a covered protocol tested the UMA oracle's ability to resolve technically nuanced disagreements post-incident.

• Audit Competition drives down costs but can compromise review depth.
• UMA Oracle provides a decentralized truth source for claim disputes.
• The system illustrates the challenge of aligning pre-event incentives with post-event accountability.

Parametric triggers use objective data (e.g., weather indices) to automate payouts, eliminating claims assessment moral hazard. However, this introduces basis risk—the mismatch between the measured parameter and actual loss. A case study in decentralized weather data oracles reveals the moral hazard for data providers and the systemic risk of oracle manipulation or failure.

• Automated Payouts remove adjudication bias and speed up compensation.
• Basis Risk is transferred from the insurer to the policyholder.
• This analysis shows the trade-off between automation fidelity and real-world loss coverage.

The protocol's use of actuarial vaults and risk tranching aims to optimize capital efficiency by matching risk appetite with yield. This can create a moral hazard where senior tranche providers, believing they are protected, under-price tail risks. Analysis of stablecoin depeg events shows how correlated liquidations can rapidly erode junior capital and threaten senior tranches, testing the model's resilience.

• Risk Tranching separates capital into senior and junior risk layers.
• Capital Efficiency attracts yield seekers but concentrates risk in junior tranches.
• The structure demonstrates how sophisticated financial engineering can inadvertently concentrate systemic risk.