How to Implement a Compliance Monitoring Dashboard

A compliance monitoring dashboard is a critical tool for Web3 projects operating in regulated environments. It aggregates and visualizes on-chain data to help teams track adherence to regulatory requirements like Anti-Money Laundering (AML) and Know Your Customer (KYC) policies. Unlike traditional finance, blockchain compliance must handle pseudonymous addresses, cross-chain activity, and smart contract interactions. The core components of such a dashboard typically include wallet screening against sanction lists, transaction monitoring for suspicious patterns, and risk scoring for counterparties.

To build this dashboard, you need to source data from multiple layers. Start with a reliable blockchain node provider or indexer like Chainscore, The Graph, or Alchemy to fetch raw transaction data. You'll then need to enrich this data with external intelligence, such as wallet labels from Chainalysis or TRM Labs, and sanction lists from official sources like the OFAC SDN list. The technical stack often involves a backend service (e.g., Node.js, Python) to ingest and process this data, a database (e.g., PostgreSQL, TimescaleDB) for storage, and a frontend framework (e.g., React, Vue.js) for visualization.

Implementing wallet screening involves checking each interacting address against known risk databases. A basic service might query an API, but for performance, you should maintain a local cache or database of high-risk addresses. For example, you could create a WalletRisk table with fields for address, risk_score, and list_source. Your ingestion service would periodically update this table and flag new transactions involving these addresses in real-time, triggering alerts.

Transaction monitoring requires analyzing patterns that may indicate illicit activity. Key heuristics to implement include: - detecting mixer or tumbler interactions (e.g., Tornado Cash), - identifying structured transactions (breaking large sums into smaller amounts), and - monitoring transactions with high-risk jurisdictions. Implementing these rules requires parsing transaction logs and calculating aggregate values over time windows, which can be efficiently done using a time-series database.

Finally, the dashboard frontend should present this information clearly. Use charts to show risk score distributions over time, tables to list flagged transactions with details like amount, involved addresses, and rule triggered, and a real-time alert panel. Ensure all data is actionable, with links to on-chain explorers for investigation. By integrating these components, you create a powerful tool for proactive compliance management in decentralized applications.

A robust data ingestion pipeline is the core prerequisite. Your dashboard must connect to on-chain data sources like block explorers (Etherscan, Arbiscan), indexers (The Graph, Dune Analytics), and node RPC endpoints. For off-chain data, integrate with centralized exchange APIs (Binance, Coinbase), KYC provider feeds (Sumsub, Jumio), and regulatory list APIs (OFAC SDN, Chainalysis). Use a message queue like Apache Kafka or AWS Kinesis to handle the high volume and velocity of this streaming data, ensuring no critical transaction is missed.

You will need a dedicated database schema designed for analytical queries. A time-series database like TimescaleDB or InfluxDB is optimal for storing transaction timestamps, gas fees, and wallet activity patterns. A separate relational database (PostgreSQL) should store entity data—user profiles, KYC statuses, and whitelisted addresses. Implement an ETL (Extract, Transform, Load) process, perhaps using Airflow or Prefect, to clean, normalize, and join this on-chain and off-chain data into a single source of truth for your dashboard's backend.

Define your compliance rules engine. This is the logic that flags transactions for review. Rules are typically written in a domain-specific language (DSL) and evaluate against ingested data. Common rules include: transaction.value > $10,000, counterparty_address IN (OFAC_LIST), or wallet.age_days < 7 AND total_volume > $50,000. Use a rules engine like Drools or a custom service built with a library like json-rules-engine to evaluate these conditions in real-time and generate alerts.

Security and access control are non-negotiable. The dashboard backend must be deployed in a secure, private cloud environment (AWS VPC, GCP). Implement role-based access control (RBAC) to ensure only authorized compliance officers can view sensitive data and override alerts. All API keys for data sources must be managed through a secrets manager (AWS Secrets Manager, HashiCorp Vault). Finally, ensure your entire stack is auditable by logging all data accesses, rule evaluations, and user actions for regulatory reporting.

A compliance monitoring dashboard aggregates and visualizes on-chain data to help organizations adhere to regulatory requirements. The core architecture typically involves a data ingestion layer that pulls raw blockchain data from nodes or indexers like The Graph, a processing engine that applies compliance rules, and a frontend visualization layer. Key metrics to track include wallet interactions with sanctioned addresses, transaction volume patterns indicative of mixing services, and the aggregate risk scores of counterparties based on their on-chain history.

Start by defining your data sources. For Ethereum, you can use direct RPC calls to an archive node via web3.js or ethers.js, or leverage a subgraph for indexed historical data. A common approach is to listen for new blocks and parse transactions and event logs for addresses of interest. For example, you can filter transactions where the to or from field matches an address on the OFAC SDN list, which should be maintained in a local database. Here's a basic ingestion snippet using ethers: const tx = await provider.getTransactionReceipt(hash); if (sanctionedAddresses.has(tx.from)) { logSanctionedTx(tx); }.

The processing layer applies your compliance logic. This involves calculating risk scores by analyzing transaction graphs, checking for interactions with high-risk DeFi protocols or privacy tools like Tornado Cash, and monitoring for patterns like rapid, round-number transfers. This logic is best deployed as a series of modular smart alerts. For instance, you could implement a module that flags any address that receives funds from more than 50 unique addresses in a 24-hour period, a potential sign of a mixing service. Processed data should be stored in a time-series database like TimescaleDB for efficient querying of historical trends.

For the dashboard frontend, use a framework like React or Vue.js with charting libraries such as Recharts or Chart.js. The UI should provide at-a-glance metrics: total flagged transactions today, a list of high-risk addresses with their associated alerts, and trend graphs for key indicators. Ensure the dashboard supports filtering by time range, blockchain, and specific risk categories. A critical feature is the ability to drill down into any alert to see the underlying transaction hash and a link to a block explorer like Etherscan for manual verification.

Finally, integrate alerting and reporting. The system should be capable of sending real-time notifications via Slack, email, or webhook when a high-severity rule is triggered. Automated reporting for periodic audits is also essential; generate PDF or CSV reports summarizing compliance events over weekly or monthly periods. Remember to design the system with scalability in mind—using message queues (e.g., RabbitMQ) for decoupling data ingestion from processing ensures the system can handle peak loads during market volatility without dropping critical transactions.

On-chain data ingestion involves pulling raw transaction and event logs directly from blockchain nodes. For Ethereum and EVM-compatible chains, you can use providers like Alchemy, Infura, or run your own Geth or Erigon node. The primary data sources are transaction receipts, event logs emitted by smart contracts, and block headers. This raw data is unstructured and must be parsed to extract meaningful fields such as sender, receiver, token amount, and contract method calls.

To automate ingestion, you need to set up listeners for specific events. For compliance, key events include large transfers (Transfer), token mints/burns, and interactions with mixing services or sanctioned addresses. Using the Ethers.js or Web3.py libraries, you can create a script that subscribes to new blocks and filters for transactions involving addresses on your watchlist. A common pattern is to use provider.on('block', ...) to trigger data fetching for each new block.

Here is a basic Node.js example using Ethers.js to listen for Transfer events on a USDC contract:

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const ethers = require('ethers');
const provider = new ethers.providers.JsonRpcProvider('YOUR_RPC_URL');
const usdcAddress = '0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48';
const usdcAbi = ['event Transfer(address indexed from, address indexed to, uint256 value)'];
const contract = new ethers.Contract(usdcAddress, usdcAbi, provider);

contract.on('Transfer', (from, to, value, event) => {
  console.log(`Transfer: ${from} -> ${to}, Value: ${value}`);
  // Add logic to check 'from' and 'to' against compliance lists
});

After capturing raw events, you must structure and normalize the data. This involves converting hexadecimal values to human-readable formats (e.g., Wei to Ether), adding timestamps from block numbers, and labeling transaction types. Store this processed data in a time-series database like TimescaleDB or a data warehouse like Google BigQuery for efficient querying. Structuring your data schema around entities (wallets, contracts) and events (transfers, swaps) from the start will make building dashboard queries significantly easier later.

For production systems, consider scalability and resilience. Raw JSON-RPC calls can be slow and rate-limited. Implement a message queue (e.g., Apache Kafka, RabbitMQ) to decouple data ingestion from processing. Use a blockchain indexer like The Graph for complex historical queries or to offload filtering logic. Always include error handling for reorgs, RPC failures, and contract ABI mismatches to ensure your data pipeline remains reliable.

The output of this step is a clean, queryable dataset of all relevant on-chain activity. This dataset becomes the single source of truth for your dashboard's analytics, alerting, and reporting features. With data flowing reliably, you can proceed to Step 2: analyzing this data for risk signals and compliance violations.

Node telemetry refers to the continuous stream of performance and health data emitted by your validator clients. This includes metrics like CPU/memory usage, disk I/O, network latency, block proposal success rate, and peer count. Unlike on-chain data, this information exists off-chain and must be actively collected. The goal of this step is to establish a reliable data pipeline that ingests this raw telemetry, transforms it into a structured format, and makes it available for analysis in your dashboard.

The most common method for collection is using Prometheus, a time-series database and monitoring toolkit. Validator clients like Lighthouse, Prysm, and Teku have built-in Prometheus metrics endpoints. You configure your node to expose metrics on a local port (e.g., http://localhost:8080/metrics). A Prometheus server then scrapes this endpoint at regular intervals, pulling the latest metric values. You can run Prometheus on the same machine as your node or on a separate monitoring server that can reach all your nodes.

For a multi-node setup, you need to configure Prometheus with a scrape_configs section targeting each of your nodes. Here is a basic example configuration snippet:

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scrape_configs:
  - job_name: 'validator-nodes'
    static_configs:
      - targets: ['node-ip-1:8080', 'node-ip-2:8080', 'node-ip-3:8080']
    scrape_interval: 15s

This tells Prometheus to collect metrics from the three listed targets every 15 seconds. Security is critical; ensure these endpoints are not publicly accessible, using firewall rules or authentication.

Once collected, the raw metrics need context. Labels in Prometheus are key-value pairs (e.g., chain="mainnet", client="lighthouse", region="us-east-1") that you attach to every metric. This allows you to filter, group, and aggregate data in your dashboard. For instance, you can compare the average memory usage of all your Lighthouse clients versus your Teku clients, or alert on high latency for nodes in a specific region. Proper labeling is essential for effective monitoring.

The final component is making this data available to your dashboard application. While you could query Prometheus directly, a common architecture uses Grafana as the visualization layer. Grafana connects to your Prometheus server as a data source. Your custom compliance dashboard, built within Grafana or as a separate web app, then queries Prometheus via its HTTP API using queries in PromQL (Prometheus Query Language) to fetch the specific telemetry needed for each chart and alert.

Beyond basic system metrics, consider ingesting client-specific logs for deeper insights. Tools like the Loki log aggregation system can collect logs from your beacon-node and validator-client services. By correlating log events (e.g., "block proposal failed") with system telemetry (high CPU at that moment), you can diagnose root causes of compliance issues like missed attestations or proposals much faster, turning raw data into actionable intelligence.

After ingesting raw transaction data from your chosen source (e.g., Chainscore API, The Graph, or direct RPC), the next step is data processing. This involves cleaning, structuring, and enriching the data. Common tasks include normalizing wallet addresses to checksum format, converting token amounts using decimals from the token contract's ABI, and parsing complex transaction logs to extract specific event data like Transfer or Swap. A robust processing pipeline handles edge cases and failed transactions to ensure data integrity before analysis.

With clean data, you can now calculate Key Performance Indicators (KPIs). These are the quantitative metrics that power your compliance dashboard. Essential DeFi compliance KPIs include: Total Value Received (TVR) from sanctioned entities, Exposure Concentration (percentage of funds from top N counterparties), Transaction Velocity (count/volume over time windows), and Anomaly Scores based on deviation from historical patterns. Each KPI should be calculated per user wallet or vault address to enable entity-level monitoring.

Implementation typically involves writing aggregation logic in your backend service. For a wallet's TVR from a sanctions list, you would filter incoming transactions where the from address matches a listed entity and sum the USD value of assets transferred. Using Python with pandas or a Node.js script, this might look like filtering a dataframe or array of transactions. It's critical to use reliable, real-time price oracles like Chainlink or decentralized APIs to convert native and ERC-20 token amounts into a stable fiat equivalent (e.g., USD) for accurate valuation.

For ongoing monitoring, these calculations must be event-driven or batch-processed regularly. Architect your system to recalculate KPIs upon receiving new block data or on a scheduled cron job. Store the results in a time-series database (like TimescaleDB) or a standard SQL database with timestamped records. This historical data is vital for tracking trends, generating compliance reports over arbitrary date ranges, and detecting whether a wallet's risk profile is improving or deteriorating over time.

Finally, consider optimization and scalability. Processing on-chain data for thousands of wallets can be computationally intensive. Use database indexing on wallet addresses and block timestamps. For complex chain analysis (e.g., tracing funds through multiple hops), you may need to implement graph traversal algorithms or leverage specialized services. The output of this step is a structured dataset of calculated KPIs, ready to be served to your dashboard's frontend via an API for visualization and alerting.

A compliance monitoring dashboard transforms raw blockchain data into actionable intelligence. The core components are a data ingestion layer that pulls events from Chainscore's API, a rules engine that evaluates transactions against your compliance policies, and an alerting module that notifies your team. For this implementation, we'll use a Node.js backend with Express to create a webhook endpoint that receives real-time transaction data from Chainscore's monitor-address endpoint. The dashboard's frontend, built with a framework like React or Vue, will display these alerts in a sortable table with key details like transaction hash, risk score, and flagged rule.

The alert logic is defined in a rules configuration file. Each rule is a function that evaluates a transaction object. For example, a rule might flag any transaction where the value exceeds a threshold of 10 ETH, or where the interacting counterparty address is on a known sanctions list. Chainscore enriches each transaction with labels and risk signals, which your rules can directly reference. Here's a simplified rule checking for high-value transfers to a new address:

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function highValueToNewCounterparty(tx, riskData) {
  const VALUE_THRESHOLD = ethers.utils.parseEther('10');
  const isHighValue = tx.value.gt(VALUE_THRESHOLD);
  const isNewCounterparty = riskData.counterpartyLabels.includes('FIRST_INTERACTION');
  return isHighValue && isNewCounterparty;
}

When a rule is triggered, the system must create an alert. This involves logging the event to a database (e.g., PostgreSQL or MongoDB) for audit trails and sending a notification. Notifications can be delivered via Slack webhook, email (using Nodemailer), or SMS (via Twilio). The alert payload should include the transaction hash, a link to a block explorer like Etherscan, the specific rule violated, and the associated risk score from Chainscore. Implementing a deduplication mechanism, such as checking for alerts on the same transaction hash within a 5-minute window, prevents alert fatigue for recurring malicious activity.

To visualize the data, the dashboard frontend needs to fetch alerts from your backend API. Key UI components include a summary overview showing total alerts, alerts by severity, and alerts over time (using a library like Chart.js), and a detailed alert log. Each alert entry should be clickable to reveal a full transaction analysis, including the decoded input data (using ethers.js), the full path of internal calls, and the Chainscore risk breakdown. For teams managing multiple entities, adding filters for specific wallet addresses, date ranges, and rule types is essential.

Finally, integrate the dashboard with your existing workflows. This can involve creating automated tickets in Jira or Linear when a high-severity alert fires, or logging all compliance events to a secure SIEM system. Regularly backtest your rules against historical transaction data to tune their sensitivity and reduce false positives. The complete code for this alerting system, including the backend server, sample rules, and a basic React frontend, is available in the Chainscore Labs GitHub repository.

The frontend is the user-facing interface that visualizes compliance data and risk metrics. For a modern, reactive application, frameworks like React or Vue.js are ideal. The core architecture involves a component-based structure where each widget—such as a risk score gauge, transaction volume chart, or flagged address list—is a reusable component. State management libraries like Redux or Zustand are crucial for handling the application's global state, including user preferences, alert filters, and live data feeds from your backend API.

Data visualization is central to the dashboard's utility. Libraries like Recharts, Chart.js, or D3.js are used to render complex charts. Key visualizations include: a time-series line chart for transaction volume, a bar chart showing risk score distribution by jurisdiction, and a network graph mapping high-risk entity relationships. Each chart component should accept data props from the state manager and update reactively. Implement tooltips, zooming, and export functionality to enhance data exploration.

For real-time updates, you must integrate WebSocket connections. After establishing a WebSocket connection to your backend service (e.g., using socket.io-client), your frontend can listen for events like new_alert or risk_score_update. When an event is received, the state manager updates the relevant data, and the React/Vue reactivity system automatically re-renders the affected components. This provides a live monitoring experience without requiring manual page refreshes.

User interaction features include filtering and alert management. Build filter components that allow users to select time ranges, risk score thresholds, or specific blockchain networks. These filter values should be dispatched to the state manager and used to query or filter the displayed data. An alerts panel should list recent compliance flags, with actions like Acknowledge or Escalate. Each action triggers a POST request to your backend API to update the alert's status.

Finally, consider security and deployment. Implement authentication, ensuring API calls include a valid JWT token. For deployment, you can build a static site using npm run build and host it on services like Vercel, Netlify, or an AWS S3 bucket. The built frontend will connect to your publicly accessible backend API and WebSocket endpoint, completing the full-stack compliance monitoring application.