Setting Up a Risk Scoring Algorithm for Policyholder Onboarding

On-chain insurance protocols require automated, transparent, and objective methods to assess policyholder risk during onboarding. A risk scoring algorithm quantifies the likelihood of a claim, enabling protocols to set appropriate premiums, coverage limits, and collateral requirements. This moves underwriting from subjective human judgment to a data-driven, reproducible process. For developers, building this system involves defining risk factors, sourcing on-chain data, and creating a mathematical model that outputs a numerical score, such as a value between 1 (low risk) and 100 (high risk).

The first step is identifying and weighting risk factors. Key on-chain metrics include: wallet age and transaction history, asset diversification, interaction frequency with DeFi protocols, past claims history (if available), and the value and type of asset to be insured. Each factor must be assigned a weight based on its predictive power for a claim event. For example, a wallet with frequent, high-value interactions with complex yield farms might carry a higher risk weight than a wallet holding only stablecoins in a well-audited lending pool.

Next, you must source and normalize the data. This involves querying blockchain data via indexers like The Graph or Covalent, and potentially integrating off-chain oracle data for real-world events. Data normalization is critical; you must convert disparate metrics (e.g., transaction count, total value locked, time periods) into a common scale, often 0 to 1, for the model to process. A Solidity snippet for a simple scoring component might look like this:

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function calculateWalletAgeScore(address _user) public view returns (uint256) {
    uint256 firstTxBlock = userFirstTransaction[_user];
    uint256 ageInBlocks = block.number - firstTxBlock;
    // Normalize: 100,000 blocks ~= 2 weeks, max score of 100
    return Math.min((ageInBlocks * 100) / 100000, 100);
}

The core of the system is the scoring model itself. A common approach is a weighted sum model: Total Score = Σ (Factor_Value * Factor_Weight). More sophisticated models may use logistic regression or machine learning classifiers trained on historical claim data. The final score determines the policy parameters. For instance, a score below 30 might result in a 1% premium and full coverage, while a score above 70 might require a 5% premium and a 50% co-pay clause. This logic is encoded in the protocol's smart contracts.

Finally, the algorithm must be integrated into the policy issuance flow. When a user requests a quote, the protocol's backend or a dedicated oracle network calls the scoring function, passes the result to the underwriting smart contract, which then calculates and offers the final premium terms. It's crucial to make the scoring logic transparent and auditable. Consider publishing the model's weights and calculations on-chain or via verifiable credentials, allowing users to understand and contest their scores, which builds trust in the decentralized insurance system.

A robust risk scoring system requires a secure data ingestion pipeline. This involves connecting to trusted data sources, which typically include on-chain data providers (like The Graph for historical transaction analysis), off-chain oracles (like Chainlink for real-world asset verification), and direct wallet connection APIs. The architecture must validate and sanitize all incoming data to prevent injection of malicious or spoofed information that could corrupt the scoring model. Data should be streamed into a secure, indexed database such as PostgreSQL or a time-series database for efficient querying during the scoring process.

The core of the system is the scoring engine, which executes the algorithm logic. This is often implemented as a serverless function (AWS Lambda, Google Cloud Functions) or a microservice to ensure scalability and isolation. The engine loads the trained model—which could be a statistical model, a machine learning model serialized with frameworks like scikit-learn or TensorFlow, or a rules-based heuristic—and applies it to the ingested policyholder data. The output is a numerical risk score and often a set of feature attributions explaining which factors (e.g., transaction frequency, asset concentration, DeFi interaction history) most influenced the score.

A critical architectural component is the orchestration and scheduling layer. This system automates the entire workflow: triggering data collection for a new applicant, executing the scoring model, and posting the result. Tools like Apache Airflow, Prefect, or cloud-native schedulers (Google Cloud Scheduler, AWS EventBridge) are used to define these pipelines as Directed Acyclic Graphs (DAGs), ensuring tasks run in the correct order and failures are handled gracefully. This layer also manages retries and alerts if data sources become unavailable.

Finally, the architecture must include a secure storage and access layer for results. Scores and the underlying decision data should be stored immutably, often on a blockchain for auditability. A common pattern is to emit an event with the score and a cryptographic proof (like a Merkle proof) from the scoring engine, which is then recorded on-chain by a smart contract. This creates a transparent, tamper-proof record. The front-end application or underwriting system can then query this on-chain state to determine policy eligibility and pricing.

A risk scoring algorithm quantifies the likelihood of a claim for a prospective policyholder. In DeFi insurance, this is critical for automated underwriting, where smart contracts must assess risk without human intervention. The core components are risk factors (on-chain data points), a scoring model (the mathematical formula), and a decision engine (rules for approval/denial). For example, a protocol might score a user's wallet based on transaction history, asset composition, and interaction with known protocols to predict their risk profile.

The first step is data sourcing. You need reliable, on-chain data feeds. Key sources include:

• Wallet History: Age, transaction volume, and frequency from an indexer like The Graph or Covalent.
• Asset Exposure: The volatility and concentration of holdings, fetched from price oracles and portfolio APIs.
• Protocol Interaction: The safety score of interacted dApps, which can be sourced from audit platforms like DefiSafety or RugDoc.
• Social/Reputation: Optional off-chain signals from decentralized identity providers like ENS or Gitcoin Passport, though these require careful integration to maintain decentralization principles.

Next, you must design the scoring model. A common approach is a weighted additive model. You assign a score (e.g., 0-100) to each risk factor and a weight representing its importance. The final risk score is the weighted sum. For instance:

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Risk Score = (Wallet_Score * 0.4) + (Asset_Score * 0.3) + (Protocol_Score * 0.3)

More advanced models might use machine learning classifiers (like logistic regression) trained on historical claim data, but this requires a significant dataset and introduces model explainability challenges.

Implementing the algorithm requires writing secure, gas-efficient Solidity code for on-chain execution, or using an off-chain oracle for complex computations. A basic Solidity function might look like this:

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function calculateRiskScore(address _user) public view returns (uint256) {
    uint256 walletScore = _getWalletAgeScore(_user);
    uint256 assetScore = _getAssetConcentrationScore(_user);
    // ... calculate other factors
    // Apply weights (using fixed-point math for precision)
    uint256 total = (walletScore * 40) + (assetScore * 30); // ... etc.
    return total / 100; // Normalize
}

For production, consider using Chainlink Functions or a dedicated oracle network to fetch and compute scores off-chain, posting the result on-chain to save gas and enable more complex logic.

Finally, integrate the score into the onboarding logic. The smart contract's applyForPolicy function should call the risk scorer and enforce rules. For example:

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require(riskScore < RISK_THRESHOLD, "Risk score too high");
// Or, implement variable pricing:
uint256 premium = BASE_PREMIUM + (riskScore * PREMIUM_MULTIPLIER);

Continuous model calibration is essential. Monitor the correlation between scores and actual claim rates, and have a governance mechanism to update risk weights or data sources. This creates a dynamic, data-driven underwriting system that improves over time.

A risk scoring algorithm is the core logic that translates raw data into a quantifiable risk assessment. For decentralized insurance, this involves analyzing a user's on-chain history—such as wallet age, transaction volume, and DeFi interaction patterns—alongside any available off-chain KYC data. The algorithm's output is a single score (e.g., 1-1000) or a risk tier (e.g., Low, Medium, High) that determines policy eligibility, premium pricing, or coverage limits. This moves underwriting from subjective judgment to a transparent, reproducible process.

Designing the algorithm begins with feature selection. Key on-chain features include: wallet tenure and activity consistency, exposure to high-risk protocols, history of liquidations or failed transactions, and social graph analysis via decentralized identity. Each feature must be quantifiable. For example, you might calculate a "DeFi Diversity Score" based on the number of unique, reputable protocols a user has interacted with over the past year, weighted by the total value locked in those interactions.

The next step is weighting and logic implementation. You must decide how each feature influences the final score. A simple model uses a weighted sum: Score = (Feature_1 * Weight_1) + (Feature_2 * Weight_2) + .... Weights are determined through statistical analysis of historical loss data or simulation. For a more dynamic approach, consider a machine learning model like a logistic regression or gradient-boosted tree, trained on a dataset of "good" and "bad" historical policyholder outcomes. Smart contracts can compute or verify scores using oracles like Chainlink Functions for complex off-chain logic.

Here is a conceptual code snippet for a basic scoring function in a Solidity-compatible format, intended for an off-chain verifier or oracle:

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function calculateRiskScore(
    address userAddress,
    uint256 walletAgeInDays,
    uint256 avgTxCountPerMonth,
    uint256 defiProtocolCount
) public pure returns (uint256 score) {
    // Base score starts at 500
    score = 500;
    // Reward older, established wallets (capped bonus)
    score += (walletAgeInDays > 365) ? 100 : (walletAgeInDays / 10);
    // Penalize very low or suspiciously high activity
    if(avgTxCountPerMonth < 2) score -= 150;
    if(avgTxCountPerMonth > 300) score -= 100;
    // Reward diversified DeFi usage
    score += (defiProtocolCount * 20);
    // Ensure score is within bounds (0-1000)
    if(score > 1000) score = 1000;
    if(score < 0) score = 0;
}

This example shows adjustable levers for risk factors, with the final score bounded between 0 and 1000.

Finally, integrate the score with your policy smart contract. The onboarding function should query the risk score—either computed on-chain for simple models or retrieved via an oracle—and enforce rules. For instance: require(riskScore >= minimumThreshold, "Risk score too high"); or premium = basePremium * (riskScore / 500);. Continuous monitoring is essential; consider implementing a mechanism to adjust scores or trigger policy review based on new on-chain behavior, ensuring the system adapts to emerging risks.

A risk scoring algorithm for on-chain insurance determines a user's eligibility and premium rate by analyzing their verifiable credentials. Smart contracts can't process raw data off-chain, so the score must be computed and attested to by a trusted oracle or a zero-knowledge proof (ZKP) system. Chainscore provides standardized, composable credentials—like transaction history, protocol interactions, and wallet age—that serve as the inputs for these algorithms. The contract's core logic receives a final, signed risk score and uses it to execute conditional logic, such as approving a policy application or setting a dynamic premium.

Setting up the integration requires a two-part architecture: an off-chain scoring engine and an on-chain verifier. The off-chain component, which could be a serverless function or dedicated service, fetches a user's aggregated credentials from the Chainscore API. It then runs them through your proprietary risk model, which might weight factors like DeFi collateralization ratios, historical liquidation events, or Sybil resistance signals. The resulting score and a proof of correct computation are then sent on-chain. For transparency and auditability, consider publishing the scoring model's logic or its hash on-chain.

The on-chain smart contract must verify the attestation's authenticity. If using an oracle like Chainlink Functions or Pyth, the contract checks the oracle's signature. For a ZKP-based approach, such as with zkSNARKs, the contract verifies a proof against a trusted verification key. A basic Solidity function might look like this:

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function underwritePolicy(address applicant, uint256 score, bytes memory signature) public {
    require(verifySignature(applicant, score, signature), "Invalid score attestation");
    require(score <= RISK_THRESHOLD, "Applicant risk too high");
    // Issue policy or calculate premium...
}

This ensures only properly scored applications are processed.

Key design considerations include score freshness and user privacy. Scores can become stale; implementing a timestamp in the attestation and rejecting old scores is crucial. For privacy, the off-chain engine can compute a score from credentials without revealing the underlying data on-chain. Using ZKPs, you can even prove a score falls within an acceptable range without disclosing the exact number. Always include a pausable mechanism and governance-controlled parameter updates (e.g., adjusting the RISK_THRESHOLD) to manage algorithm upgrades and respond to market changes.

To test your integration, use Chainscore's testnet credentials and a local development network like Foundry or Hardhat. Simulate various user profiles to ensure your contract correctly accepts low-risk scores and rejects high-risk ones. Finally, consider the gas cost of verification; ZK proof verification can be expensive, so factor this into your policy's economics. By following this pattern, you can build a robust, transparent, and automated risk assessment layer directly into your insurance protocol's smart contracts.