Actuarial Models for DeFi Insurance Pricing

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Actuarial Models for DeFi Insurance Pricing

Core Actuarial Concepts in DeFi

Foundational mathematical and statistical principles adapted from traditional finance to price and manage risk in decentralized insurance protocols.

Loss Frequency & Severity

Loss Frequency measures how often a claimable event occurs, while Loss Severity quantifies the financial impact per event. These are the building blocks of risk modeling.

Frequency is modeled using distributions like Poisson or Negative Binomial.
Severity often follows heavy-tailed distributions like Pareto or Log-Normal.
Combined, they form the aggregate loss distribution, which is crucial for setting premiums and capital reserves in protocols like Nexus Mutual or InsurAce.

Technical Provisions & Reserving

Technical Provisions are liabilities a protocol must hold to cover future claims and expenses. Reserving is the actuarial process of calculating these amounts.

Includes Incurred But Not Reported (IBNR) reserves for unknown claims.
Requires stochastic modeling to account for uncertainty in claim timing and size.
Adequate reserves are critical for protocol solvency and are often held in over-collateralized vaults or yield-generating assets.

Risk Pooling & Law of Large Numbers

Risk Pooling combines uncorrelated risks from many participants to reduce overall volatility. This leverages the Law of Large Numbers, which states that average losses stabilize as pool size increases.

Enables predictable aggregate loss outcomes from unpredictable individual events.
Mitigates the impact of large, idiosyncratic claims on the pool's stability.
This principle underpins the viability of peer-to-peer coverage models in DeFi, allowing for sustainable premium pricing.

Pricing Models & Premium Calculation

Actuarial pricing determines the fair premium required to cover expected losses, expenses, and a risk margin. In DeFi, this often uses Stochastic Modeling and Monte Carlo Simulations.

Premium = Expected Loss + Risk Load + Protocol Fee.
Simulations model thousands of potential future states based on smart contract exploits, oracle failures, or slashing events.
Models must be frequently recalibrated with on-chain data to reflect evolving risk landscapes.

Capital Adequacy & Solvency

Solvency is a protocol's ability to meet its long-term financial obligations. Capital Adequacy refers to holding sufficient capital (often staked tokens) above technical provisions to absorb unexpected losses.

Measured by metrics like the Solvency Capital Requirement (SCR) or Minimum Capital Requirement (MCR).
Capital acts as a buffer against volatility and model error, protecting policyholders.
Protocols use mechanisms like staking, reinsurance, or risk-linked bonds (like those in Sherlock) to enhance capital strength.

Exposure Metrics & Risk Assessment

Quantifying a protocol's Exposure to specific risks is essential for underwriting. This involves assessing the Total Value Locked (TVL), smart contract complexity, and dependency risks.

Exposure = Probability of Failure * Value at Risk.
For smart contract coverage, metrics include code audit scores, time since deployment, and complexity of external integrations.
Accurate exposure assessment allows for risk-based pricing, where riskier protocols command higher premiums.

Building a DeFi Actuarial Model

Process overview

Define the Risk Pool and Coverage Parameters

Establish the foundational scope and terms of the insurance product.

Detailed Instructions

First, explicitly define the risk pool your model will cover, such as smart contract failure for a specific protocol (e.g., Aave V3) or exchange rate depeg for a stablecoin (e.g., USDC). Specify the coverage parameters: the maximum sum insured per policy, the policy duration (e.g., 30-day epochs), the deductible amount, and the precise trigger conditions for a valid claim. This step requires analyzing historical incident data to understand loss frequency and severity bounds. For a smart contract cover, the trigger might be a successful governance vote confirming an exploit on Snapshot.

Sub-step 1: Select the protocol and version (e.g., Compound v2, Uniswap V3).
Sub-step 2: Determine the coverage ceiling per wallet (e.g., 10 ETH).
Sub-step 3: Define the claim validation mechanism (e.g., multi-sig oracle or on-chain proof).

solidity
// Example struct for a policy parameter set
struct CoverageParams {
    address protocolAddress;
    uint256 maxCoverage;
    uint256 policyPeriod;
    uint256 deductible;
    bytes4 claimFunctionSelector;
}

Tip: Use on-chain data from platforms like Tenderly or Etherscan to audit historical transactions for the target protocol to inform realistic parameter limits.

Collect and Process Historical Loss Data

Gather and clean event data to calculate loss frequency and severity.

Detailed Instructions

Source historical data on incidents relevant to your risk pool. For smart contract risk, compile a list of verified exploits from Rekt.news, DeFiLlama's hack dashboard, and insurance claim payouts from Nexus Mutual or InsurAce. For stablecoin depeg risk, collect hourly price feeds from Chainlink oracles for assets like DAI or FRAX during past stress events (e.g., USDC depeg in March 2023). Calculate the loss frequency (events per year) and severity distribution (percentage of total value locked lost). This often requires parsing JSON event logs from The Graph or Dune Analytics. Clean the data by removing outliers and normalizing for TVL at the time of incident.

Sub-step 1: Query a Dune Analytics dashboard for "DeFi Exploits" to get a structured dataset.
Sub-step 2: Calculate the annualized incident rate: (Number of incidents in pool / Total years of protocol operation).
Sub-step 3: For each incident, compute the loss severity as (Amount Lost / TVL at time of incident).

python
# Example Python snippet to calculate annual loss frequency
import pandas as pd
# Assume 'incidents_df' has columns ['date', 'protocol', 'amount_lost', 'tvl']
start_date = incidents_df['date'].min()
days_observed = (pd.Timestamp.now() - start_date).days
years_observed = days_observed / 365.25
annual_frequency = len(incidents_df) / years_observed
print(f"Annual Loss Frequency: {annual_frequency:.2f}")

Tip: When data is sparse, use Bayesian methods to incorporate prior beliefs or data from analogous traditional finance instruments.

Model the Loss Distribution and Calculate Pure Premium

Fit a statistical distribution to the loss data and derive the base cost of risk.

Detailed Instructions

Using the processed historical data, fit a probability distribution to model the aggregate loss. The frequency component is often modeled with a Poisson or Negative Binomial distribution. The severity component (size of each loss) can be modeled with heavy-tailed distributions like Lognormal or Pareto. Use maximum likelihood estimation (MLE) or method of moments for parameter fitting. The pure premium is the expected annual loss per unit of coverage, calculated as the mean of the aggregate loss distribution (Expected Frequency * Expected Severity). For example, if the expected number of claims per year for a 100 ETH pool is 0.5, and the average severity is 20% of TVL, the pure premium is 0.5 * 0.20 = 0.10, or 10 ETH per year for the pool.

Sub-step 1: Use a Python library like scipy.stats to fit a Lognormal distribution to your severity data.
Sub-step 2: Simulate the aggregate annual loss by drawing from the frequency and severity distributions (Monte Carlo).
Sub-step 3: Calculate the pure premium as the empirical mean of your simulation results.

python
import numpy as np
from scipy.stats import poisson, lognorm
# Parameters (example)
lambda_freq = 0.5  # Expected claims per year
mu_sev, sigma_sev = np.log(0.15), 0.8  # Lognormal params for severity
# Monte Carlo simulation
n_sim = 100000
freq_samples = poisson.rvs(lambda_freq, size=n_sim)
agg_loss = np.array([np.sum(lognorm.rvs(sigma_sev, scale=np.exp(mu_sev), size=n)) for n in freq_samples])
pure_premium = np.mean(agg_loss)
print(f"Pure Premium (Expected Annual Loss): {pure_premium:.4f}")

Tip: Validate your model using back-testing against a hold-out sample of historical data to check calibration.

Add Loadings and Determine Final Premium

Adjust the pure premium for expenses, profit, and risk margin to set the final price.

Detailed Instructions

The pure premium only covers expected losses. You must add loadings to create a viable commercial premium. The expense loading covers operational costs (oracle fees, gas, development) and is often a fixed percentage (e.g., 15%). The risk loading (or safety loading) provides a buffer for volatility and uncertainty, calculated via the standard deviation or Value at Risk (VaR) of the loss distribution. A common method is to use the standard deviation principle: Risk Loading = β * σ, where σ is the standard deviation of aggregate losses. Finally, a profit loading (e.g., 5%) is added. The final premium per unit of coverage is: Final Premium = Pure Premium + Expense Loading + Risk Loading + Profit Loading. This premium is then converted to a periodic (e.g., monthly) rate for users.

Sub-step 1: Calculate the standard deviation of the aggregate loss from your Monte Carlo simulation (np.std(agg_loss)).
Sub-step 2: Apply a risk loading factor β (e.g., 0.3) to the standard deviation.
Sub-step 3: Sum all loadings and the pure premium to get the total technical premium.

python
# Continuing from previous Python simulation
expense_ratio = 0.15
risk_factor_beta = 0.3
profit_margin = 0.05
loss_std = np.std(agg_loss)
risk_loading = risk_factor_beta * loss_std
expense_loading = expense_ratio * pure_premium
profit_loading = profit_margin * pure_premium
final_premium = pure_premium + expense_loading + risk_loading + profit_loading
print(f"Final Premium per unit: {final_premium:.4f}")

Tip: The risk loading factor β can be calibrated to target a specific solvency probability (e.g., 99.5% chance of covering all claims) using your simulated loss distribution.

Implement and Automate the Model On-Chain

Deploy smart contracts that calculate dynamic premiums based on the model's logic.

Detailed Instructions

Translate your actuarial model into a smart contract system that can calculate premiums dynamically. Create a PremiumCalculator contract that stores or receives updated model parameters (frequency λ, severity distribution params, loadings). The contract should calculate premiums in real-time, potentially adjusting for current pool utilization (total coverage active / capital available) to manage risk exposure. Use oracles like Chainlink to feed in real-time data, such as protocol TVL from DefiLlama's API, which can affect severity assumptions. Implement a function calculatePremium(uint256 _coverAmount) that returns the premium for a given policy amount, applying the formula from previous steps. Ensure the contract uses fixed-point math libraries (e.g., PRBMath) for precision.

Sub-step 1: Write a Solidity function that computes the pure premium using stored parameters and the requested cover amount.
Sub-step 2: Integrate a utilization multiplier: if pool utilization > 80%, increase the risk loading.
Sub-step 3: Add a function for governance (e.g., a DAO) to update the model parameters based on new loss data.

solidity
// Simplified excerpt of a premium calculator contract
import "@prb/math/PRBMathUD60x18.sol";

contract ActuarialPremiumCalculator {
    using PRBMathUD60x18 for uint256;
    
    uint256 public lambdaFreq; // Annual expected frequency
    uint256 public expSeverity; // Expected severity (as a decimal fraction)
    uint256 public expenseLoading; // e.g., 0.15 for 15%
    
    function calculatePremium(uint256 _coverAmount, uint256 _poolUtilization) public view returns (uint256 premium) {
        uint256 purePremium = _coverAmount.mul(lambdaFreq).mul(expSeverity);
        uint256 basePremium = purePremium + purePremium.mul(expenseLoading);
        // Apply risk loading based on utilization
        if (_poolUtilization > 0.8e18) { // 80% in 18-decimal fixed point
            basePremium = basePremium.mul(1.1e18); // Add 10% loading
        }
        return basePremium;
    }
}

Tip: Consider making premium updates occur over epochs (e.g., weekly) via an oracle or keeper to prevent front-running and gas inefficiency from constant recalculation.

On-Chain and Off-Chain Data Sources for Risk Modeling

Comparison of data source characteristics for actuarial modeling in DeFi insurance.

Data Source	Latency	Cost to Access	Verifiability	Primary Use Case
Ethereum Mainnet Block Data	~12 seconds per block	Gas fees for RPC calls	Fully verifiable via consensus	Smart contract state, transaction history
Chainlink Price Feeds	1-60 seconds per update	Free to read, cost to deploy	Cryptographically signed by oracles	Real-time asset pricing for liquidation triggers
The Graph Subgraph Indexing	Near real-time (indexed)	Query fee based on usage	Provenance tracked via IPFS	Historical event data and aggregated metrics
Dune Analytics Queries	Minutes to hours (cached)	Free tier, paid for compute	Relies on third-party indexing	Ad-hoc analysis and dashboarding
Traditional Market APIs (e.g., CoinGecko)	Seconds to minutes	API key with rate limits	Centralized, requires trust	Broader market sentiment, volatility data
MEV Relay Data (e.g., Flashbots)	Sub-second for pending tx	Private API access	Partially verifiable post-inclusion	Frontrunning and sandwich attack risk assessment
Insurance Claim History (Off-Chain DB)	Batch updates (daily)	Internal infrastructure cost	Auditable via merkle proofs	Historical loss ratios and claim frequency

DeFi Insurance Pricing Models

Core Principles of Risk Pricing

Actuarial science applies statistical methods to assess risk and determine insurance premiums. In DeFi, this involves quantifying the probability and potential financial impact of protocol failures, smart contract exploits, and oracle malfunctions. Unlike traditional insurance with long historical data, DeFi models must adapt to rapidly evolving, on-chain environments.

Key Risk Factors

Protocol Risk: The inherent vulnerability of a DeFi protocol's codebase and economic design, such as the complexity of a lending platform's liquidation engine.
Custodial Risk: For wrapped assets or cross-chain bridges, the risk associated with the custodian's security and solvency.
Oracle Risk: The probability of price feed manipulation or failure, which can trigger incorrect liquidations or swaps.

Example

When pricing coverage for a MakerDAO vault, an actuary models the chance of an ETH price crash exceeding the collateralization ratio before liquidation occurs, factoring in network congestion that could delay keeper bots.

Challenges in DeFi Actuarial Modeling

Key obstacles in applying traditional actuarial science to decentralized insurance protocols.

Data Scarcity and Quality

On-chain data is often sparse and lacks historical depth for long-tail risks.

Smart contract exploits have limited historical frequency data.
Oracles can provide flawed or manipulated price feeds.
Incomplete data leads to unreliable loss probability estimates, forcing reliance on assumptions.

Dynamic and Correlated Risk

Systemic risk in DeFi creates non-independent, cascading failure modes.

A protocol hack can trigger liquidations and depeg events across connected protocols.
Market volatility directly impacts collateralized positions simultaneously.
Modeling requires complex dependency networks, not simple independent probability distributions.

Parameterizing Smart Contract Risk

Quantifying the failure probability of immutable smart contract code.

Requires auditing depth, complexity analysis, and upgrade mechanism scrutiny.
Must account for novel attack vectors like flash loan manipulations.
Premiums must be calibrated for both known vulnerabilities and unknown future exploits.

Capital Efficiency vs. Solvency

Balancing capital lock-up in pools with claims-paying ability during black swan events.

Over-collateralization reduces capital efficiency for liquidity providers.
Under-collateralization risks protocol insolvency during mass claims.
Models must dynamically adjust capital requirements based on real-time risk exposure.

Regulatory and Legal Uncertainty

Ambiguous regulatory frameworks for decentralized insurance complicate risk assessment.

Uncertainty around policy enforceability and jurisdictional claims handling.
Potential for regulatory actions that could impact protocol operations or asset backing.
This non-quantifiable legal risk must be factored into premium pricing and reserves.

Adverse Selection and Moral Hazard

Managing information asymmetry and perverse incentives in a permissionless system.

Sophisticated users may insure assets they know are vulnerable (adverse selection).
Coverage could reduce incentive for secure practices (moral hazard).
Models require mechanisms like waiting periods, co-payments, or risk-based pricing to mitigate.

Implementing a Pricing Model On-Chain

Process overview for deploying and integrating actuarial logic into a smart contract system.

Define the Core Pricing Function

Formalize the mathematical model and translate it into a Solidity function.

Detailed Instructions

Begin by isolating the core pricing formula from your actuarial model. This is often a function of variables like expected loss, capital cost, and a profit margin. For a simple model, this could be Premium = Expected Loss * (1 + Risk Load) + Operational Cost. In Solidity, you must account for fixed-point arithmetic, as the language lacks native decimals. Decide on a precision factor, commonly 1e18 (for 18 decimals, like ETH).

Sub-step 1: Write the function signature, e.g., function calculatePremium(uint256 expectedLoss, uint256 riskLoadBps, uint256 operationalCost) public pure returns (uint256 premium).
Sub-step 2: Implement the math using the chosen precision. For a risk load of 15% (1500 basis points), the calculation becomes premium = expectedLoss * (1e18 + 1500 * 1e14) / 1e18 + operationalCost;.
Sub-step 3: Add require statements to validate inputs, such as require(riskLoadBps <= 10000, "Risk load > 100%");.

solidity
// Example: Basic Premium Calculation
function calculatePremium(
    uint256 expectedLoss,
    uint256 riskLoadBps, // e.g., 1500 for 15%
    uint256 operationalCost
) public pure returns (uint256) {
    require(riskLoadBps <= 10000, "Invalid risk load");
    uint256 riskFactor = 1e18 + (riskLoadBps * 1e14); // 1e14 = 1e18 / 10000
    uint256 premium = (expectedLoss * riskFactor) / 1e18 + operationalCost;
    return premium;
}

Tip: Use a library like PRBMath for more complex operations (e.g., exponentiation) to avoid overflow and ensure precision.

Integrate Oracle Data Feeds

Connect the pricing function to real-world data for dynamic parameter updates.

Detailed Instructions

On-chain models require external data for variables like historical loss ratios or asset volatility. Use a decentralized oracle network, such as Chainlink, to fetch this data reliably. The key is to design your contract to request and receive data updates, which then recalibrate your model's parameters.

Sub-step 1: Identify the necessary data points and their sources. For a crypto-native insurance product, you might need the 30-day rolling average of exchange hack losses from a custom adapter.
Sub-step 2: Import the oracle interface (e.g., AggregatorV3Interface for Chainlink price feeds) and store the contract address for the data feed.
Sub-step 3: Create an internal function, often callable by a keeper or via time-based triggers, that calls latestRoundData() on the feed and updates a state variable like currentLossRatio.

solidity
// Example: Fetching Data from a Chainlink Feed
import "@chainlink/contracts/src/v0.8/interfaces/AggregatorV3Interface.sol";

contract PricingModel {
    AggregatorV3Interface internal lossRatioFeed;
    uint256 public currentLossRatio;

    constructor(address _feedAddress) {
        lossRatioFeed = AggregatorV3Interface(_feedAddress);
    }

    function updateLossRatio() public {
        (
            uint80 roundId,
            int256 answer,
            uint256 startedAt,
            uint256 updatedAt,
            uint80 answeredInRound
        ) = lossRatioFeed.latestRoundData();
        require(answer > 0, "Invalid feed answer");
        currentLossRatio = uint256(answer); // Assumes answer is already scaled
    }
}

Tip: Always include checks for stale data by verifying updatedAt is within a permissible time window (e.g., 24 hours).

Implement Access Control and Parameter Management

Secure the model's configuration with role-based permissions for updates.

Detailed Instructions

Critical model parameters must be updatable to respond to market conditions, but changes must be controlled to prevent manipulation. Implement an access control system, typically using OpenZeppelin's libraries, to designate a risk manager or governance address.

Sub-step 1: Inherit from Ownable or AccessControl and define a role like RISK_MANAGER_ROLE.
Sub-step 2: Store key parameters as state variables (e.g., baseRiskLoad, minPremium) and create setter functions that are restricted to the authorized role.
Sub-step 3: Incorporate a time-lock mechanism for sensitive parameter changes. This can be a simple require(block.timestamp >= changeEffectiveTime) check to allow users to react.

solidity
// Example: Managed Parameters with AccessControl
import "@openzeppelin/contracts/access/AccessControl.sol";

contract ManagedPricing is AccessControl {
    bytes32 public constant RISK_MANAGER_ROLE = keccak256("RISK_MANAGER_ROLE");
    uint256 public baseRiskLoad; // Stored in basis points
    uint256 public changeEffectiveTime;

    constructor(address admin) {
        _grantRole(DEFAULT_ADMIN_ROLE, admin);
        _grantRole(RISK_MANAGER_ROLE, admin);
        baseRiskLoad = 2000; // Initial 20%
    }

    function setBaseRiskLoad(uint256 newLoad, uint256 effectiveAfter) public onlyRole(RISK_MANAGER_ROLE) {
        require(newLoad <= 5000, "Load too high"); // Cap at 50%
        baseRiskLoad = newLoad;
        changeEffectiveTime = block.timestamp + effectiveAfter;
    }

    function getActiveRiskLoad() public view returns (uint256) {
        if(block.timestamp < changeEffectiveTime) {
            // Return old load until time lock passes
            return baseRiskLoad;
        }
        return baseRiskLoad;
    }
}

Tip: For complex governance, consider integrating a timelock controller contract that queues transactions before execution.

Test and Simulate with Forked Mainnet

Validate the model's behavior and economic security using forked blockchain environments.

Detailed Instructions

Deploying untested pricing logic risks immediate financial loss. Use a development framework like Foundry or Hardhat to run tests on a forked mainnet environment. This allows you to simulate the contract's operation with real-world data and state.

Sub-step 1: Set up a forked network in your test configuration. In Foundry, use --fork-url $RPC_URL; in Hardhat, configure the forking network in hardhat.config.js.
Sub-step 2: Write comprehensive test cases. Test edge cases like maximum input values, oracle failure modes, and reentrancy attempts on the premium payment function.
Sub-step 3: Perform invariant testing or fuzzing. For example, assert that the calculated premium is always greater than or equal to the expected loss under all valid random inputs.

solidity
// Example Foundry Test for Pricing Function
import "forge-std/Test.sol";
import "../src/PricingModel.sol";

contract PricingModelTest is Test {
    PricingModel model;

    function setUp() public {
        vm.createSelectFork(vm.envString("MAINNET_RPC_URL")); // Fork mainnet
        model = new PricingModel(address(0)); // Use mock oracle in practice
    }

    function test_PremiumExceedsLoss() public {
        // Fuzz test with random inputs
        uint256 loss = bound(loss, 1e18, 1000e18); // Random value between 1 and 1000 ETH
        uint256 load = bound(load, 0, 10000); // 0% to 100%
        uint256 cost = 1e17; // 0.1 ETH operational cost

        uint256 premium = model.calculatePremium(loss, load, cost);
        assertGe(premium, loss + cost); // Premium >= Loss + Cost
    }

    function test_RevertOnInvalidRiskLoad() public {
        vm.expectRevert("Invalid risk load");
        model.calculatePremium(1e18, 10001, 0); // 100.01% load
    }
}

Tip: Use vm.roll(block.number + 100) to simulate the passage of blocks and test time-dependent logic like parameter time-locks.

Deploy and Monitor with Event Emission

Launch the contract and implement logging to track pricing decisions and parameter changes.

Detailed Instructions

Final deployment to a live network requires careful verification and ongoing monitoring. Use a scripted deployment process and ensure your contract emits events for all critical state changes, which are essential for off-chain monitoring and analytics.

Sub-step 1: Write a deployment script that sets initial parameters and grants roles. Verify the contract source code on a block explorer like Etherscan after deployment.
Sub-step 2: Define and emit events in your contract. Key events include PremiumCalculated(address indexed user, uint256 loss, uint256 premium), RiskLoadUpdated(uint256 oldValue, uint256 newValue), and OracleUpdated(address newOracle).
Sub-step 3: Set up off-chain monitoring. Use a service like The Graph to index these events into a queryable subgraph, or run a script that listens for events and alerts on anomalies (e.g., a premium spike exceeding 3 standard deviations from the mean).

solidity
// Example: Contract with Event Emission
contract AuditablePricingModel {
    event PremiumCalculated(address indexed policyHolder, uint256 coveredAmount, uint256 premium, uint256 timestamp);
    event ParameterUpdated(string paramName, uint256 oldValue, uint256 newValue);

    uint256 public constant OPERATIONAL_COST = 0.1 ether;

    function quotePremium(address policyHolder, uint256 coveredAmount, uint256 riskLoad) public returns (uint256) {
        uint256 premium = calculatePremium(coveredAmount, riskLoad, OPERATIONAL_COST);

        // Emit event for transparency and monitoring
        emit PremiumCalculated(policyHolder, coveredAmount, premium, block.timestamp);
        return premium;
    }

    function updateParameter(string memory name, uint256 newValue) internal {
        uint256 oldValue = getParameter(name);
        // ... update logic ...
        emit ParameterUpdated(name, oldValue, newValue);
    }
}

Tip: Consider deploying first to a testnet and using a staging environment where governance proposals can be simulated before mainnet launch.

SECTION-FAQ

DeFi Actuarial Modeling FAQ

Further Resources and Research

Nexus Mutual Documentation

Details Nexus Mutual's capital model, risk assessment process, cover pricing mechanics, and assumptions used for on-chain actuarial decisions.

Visit resource

Etherisc Protocol Documentation

Covers decentralized insurance primitives, risk pools, premium calculation logic, and oracle-driven payout models used in actuarial design.

Visit resource

Nexus Mutual Risk Research Blog

Articles analyzing smart contract risk, probability of loss estimation, capital efficiency, and pricing methodology updates.

Visit resource

DeFiLlama Insurance Analytics

Aggregated metrics on DeFi insurance protocols including TVL, active cover, and historical usage for empirical risk analysis.

Visit resource

Nexus Mutual Smart Contracts Repository

Source code for Nexus Mutual's core contracts, including components related to cover creation, capital pools, and risk parameters.

Visit resource

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