Real-Time AML Monitoring for Multi-Chain Crypto Platforms

Anti-Money Laundering (AML) monitoring in the cryptocurrency sector has evolved from reactive, periodic reviews to proactive, real-time surveillance systems. With crypto-related money laundering reaching $2.1 billion in stolen funds in 2024 alone, implementing robust Live AML monitoring is no longer optional—it's a survival requirement for crypto platforms. ## The Evolution of Crypto AML ### Traditional AML (2015-2020) **Characteristics:** - Periodic batch reviews (daily/weekly) - Manual transaction analysis - Limited blockchain coverage (Bitcoin only) - Reactive approach (after-the-fact detection) - High false positive rates (40-60%) - Days-to-weeks investigation cycles **Limitations:** - Criminals complete money laundering before detection - Cross-chain obfuscation not detected - Limited scalability (manual review bottleneck) - Poor user experience (delayed withdrawals) ### Modern Live AML (2021-2025) **Characteristics:** - Real-time transaction monitoring (<2 seconds) - AI-powered behavioral analysis - Multi-chain coverage (all popular blockchains) - Proactive risk prevention - Low false positive rates (5-10%) - Instant automated decisions **Advantages:** - Prevention before fund movement - Complex obfuscation pattern detection - Unlimited scalability (cloud-native) - Seamless user experience (instant approvals) ## Live AML Architecture ### System Components ``` ┌─────────────────────────────────────────────┐ │ Multi-Chain Blockchain Indexers │ │ (Real-time transaction monitoring) │ │ • Bitcoin • Ethereum • Binance Smart Chain │ │ • Polygon • Avalanche • Solana • Tron │ │ • Arbitrum • Optimism • Base │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Transaction Classification Engine │ │ • Deposit Detection │ │ • Withdrawal Monitoring │ │ • Internal Transfers │ │ • Smart Contract Interactions │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Address Clustering & Labeling │ │ • Known Exchange Addresses │ │ • Mixer/Tumbler Services │ │ • Sanctioned Entities │ │ • DeFi Protocols │ │ • Gambling Sites │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Hop Analysis (2-5 hops) │ │ • Direct Source/Destination │ │ • Intermediate Wallets │ │ • Taint Analysis │ │ • Fund Flow Mapping │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Risk Scoring Engine │ │ • Machine Learning Models │ │ • Behavioral Pattern Analysis │ │ • Historical Risk Data │ │ • Real-time Risk Calculation (0-100) │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Decision Engine │ │ • Auto-Approve (Risk 0-30) │ │ • Manual Review (Risk 31-70) │ │ • Auto-Reject (Risk 71-100) │ │ • SAR Filing Trigger (Risk 85+) │ └──────────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ Compliance & Reporting │ │ • SAR (Suspicious Activity Reports) │ │ • Audit Trails │ │ • Regulatory Reports │ │ • Analytics Dashboard │ └─────────────────────────────────────────────┘ ``` ### Real-Time Processing Pipeline **Sub-2-Second Analysis:** ```javascript // Live AML processing flow async function processTransaction(tx) { const startTime = Date.now(); // Step 1: Basic validation (50ms) const validation = await validateTransaction(tx); // Step 2: Address classification (100ms) const addressInfo = await classifyAddresses({ source: tx.from, destination: tx.to }); // Step 3: Hop analysis (300ms) const hopAnalysis = await analyzeHops({ address: tx.from, depth: 3, // Analyze 3 hops back blockchain: tx.blockchain }); // Step 4: Sanctions screening (100ms) const sanctionsCheck = await screenSanctions([ tx.from, tx.to, ...hopAnalysis.intermediateAddresses ]); // Step 5: Risk scoring (200ms) const riskScore = await calculateRiskScore({ transaction: tx, addressInfo, hopAnalysis, sanctionsCheck, userHistory: await getUserHistory(tx.userId) }); // Step 6: Decision (50ms) const decision = await makeDecision(riskScore); const totalTime = Date.now() - startTime; console.log(`AML analysis completed in ${totalTime}ms`); // Typical: 800-1800ms (well under 2 second target) return { allowed: decision.approved, riskScore: riskScore.score, riskFactors: riskScore.factors, processingTime: totalTime }; } ``` ## Multi-Chain Coverage ### Supported Blockchains **Defy Live AML covers all popular blockchains:** 1. **Bitcoin (BTC)** - UTXO-based analysis - Transaction graph analysis - CoinJoin detection - Taproot address support 2. **Ethereum (ETH)** - ERC-20 token tracking - Smart contract interaction analysis - DeFi protocol integration - MEV transaction detection 3. **Binance Smart Chain (BSC)** - BEP-20 token tracking - PancakeSwap liquidity analysis - Cross-chain bridge monitoring 4. **Polygon (MATIC)** - High-throughput transaction monitoring - DeFi ecosystem coverage - Bridge transaction analysis 5. **Avalanche (AVAX)** - C-Chain transaction monitoring - Subnet support - Cross-chain bridges 6. **Solana (SOL)** - SPL token tracking - High-speed transaction analysis - Serum DEX monitoring 7. **Tron (TRX)** - TRC-20 token tracking - High-volume transaction monitoring - USDT-TRC20 focus 8. **Arbitrum (ARB)** - Layer 2 transaction monitoring - Ethereum mainnet relationship - DeFi protocol coverage 9. **Optimism (OP)** - Layer 2 rollup analysis - Ethereum state synchronization ### Cross-Chain Analysis **Bridging Detection:** ```javascript // Detect cross-chain money laundering async function detectCrossChainObfuscation(tx) { // Example: ETH → Wrapped Bitcoin → BSC → Mixer const bridges = [ { from: 'ethereum', to: 'binance-smart-chain', protocol: 'wormhole' }, { from: 'ethereum', to: 'polygon', protocol: 'polygon-bridge' }, { from: 'ethereum', to: 'avalanche', protocol: 'avalanche-bridge' }, { from: 'bitcoin', to: 'ethereum', protocol: 'wbtc' } ]; // Trace fund flow across chains const crossChainPath = await traceCrossChain({ startAddress: tx.from, startChain: tx.blockchain, maxHops: 5, includeBridges: bridges }); // Analyze for obfuscation patterns const obfuscationScore = calculateObfuscation(crossChainPath); return { crossChainDetected: crossChainPath.length > 1, path: crossChainPath, obfuscationScore, // 0-100 suspiciousPattern: obfuscationScore > 70 }; } ``` ## Mixer and Privacy Protocol Detection ### What are Mixers? Mixers (tumblers) are services that pool multiple users' cryptocurrency and redistribute them to obscure transaction trails. **Known Mixers:** - Tornado Cash (Ethereum) - Sanctioned - Blender.io (Bitcoin) - Sanctioned - ChipMixer (Bitcoin) - Shut down - Sinbad (Bitcoin) - Sanctioned - Bitcoin Fog (Bitcoin) - Operator arrested ### Detection Techniques #### 1. Direct Interaction Detection **1M+ Known Mixer Addresses:** ```javascript const mixerDatabase = { 'ethereum': [ '0x12345... (Tornado Cash ETH 0.1 pool)', '0x23456... (Tornado Cash ETH 1 pool)', '0x34567... (Tornado Cash ETH 10 pool)', // ... thousands more ], 'bitcoin': [ '1A1zP1... (ChipMixer)', '3J98t1... (Blender.io)', // ... thousands more ] }; async function checkDirectMixerInteraction(address, blockchain) { const knownMixers = mixerDatabase[blockchain]; // Check if address is a known mixer if (knownMixers.includes(address)) { return { isMixer: true, mixerName: await getMixerName(address), riskLevel: 'CRITICAL', action: 'REJECT' }; } // Check transaction history for mixer interactions const transactions = await getTransactionHistory(address, blockchain); const mixerInteractions = transactions.filter(tx => knownMixers.includes(tx.to) || knownMixers.includes(tx.from) ); if (mixerInteractions.length > 0) { return { isMixer: false, hasM mixerContact: true, interactionCount: mixerInteractions.length, riskLevel: 'HIGH', action: 'MANUAL_REVIEW' }; } return { isMixer: false, hasMixerContact: false }; } ``` #### 2. Behavioral Pattern Analysis **Mixer-Like Behavior Detection:** ```python # Machine learning model for mixer detection import tensorflow as tf def extract_mixer_features(address): """Extract features indicating mixer-like behavior""" return { # Transaction patterns 'tx_count_24h': count_transactions(address, hours=24), 'unique_counterparties': count_unique_addresses(address), 'avg_tx_amount': calculate_average_amount(address), 'std_tx_amount': calculate_std_deviation(address), # Timing patterns 'tx_regularity_score': analyze_timing_regularity(address), 'burst_tx_count': detect_burst_transactions(address), # Amount patterns 'round_number_ratio': calculate_round_amounts(address), 'peel_chain_detected': detect_peel_chain(address), # Network topology 'clustering_coefficient': calculate_clustering(address), 'betweenness_centrality': calculate_betweenness(address), # Privacy protocol usage 'stealth_address_usage': detect_stealth_addresses(address), 'ring_signature_detected': detect_ring_signatures(address) } # Train mixer detection model model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.3), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile( optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall'] ) # Predict mixer probability features = extract_mixer_features(suspicious_address) mixer_probability = model.predict([features])[0][0] if mixer_probability > 0.85: flag_as_mixer(suspicious_address) ``` #### 3. Peel Chain Detection **What is a Peel Chain?** A peel chain is a money laundering technique where criminals send most funds to a new address while "peeling off" small amounts to cash out. **Detection:** ```javascript async function detectPeelChain(address) { const txHistory = await getTransactionHistory(address, { limit: 100 }); let peelChainScore = 0; let currentAddress = address; const chain = []; for (let i = 0; i < txHistory.length; i++) { const tx = txHistory[i]; // Check if transaction follows peel pattern: // 1. Large output (>90%) to new address (change) // 2. Small output (<10%) to different address (peel) const outputs = tx.outputs.sort((a, b) => b.amount - a.amount); if (outputs.length === 2) { const largeOutput = outputs[0]; const smallOutput = outputs[1]; const ratio = smallOutput.amount / largeOutput.amount; if (ratio < 0.1 && ratio > 0.01) { // 1-10% peel peelChainScore++; chain.push({ txHash: tx.hash, changeAddress: largeOutput.address, peelAddress: smallOutput.address, peelAmount: smallOutput.amount, peelRatio: ratio }); // Follow the chain currentAddress = largeOutput.address; } else { break; // Chain broken } } } return { isPeelChain: peelChainScore >= 3, // At least 3 consecutive peels chainLength: peelChainScore, totalPeeledAmount: chain.reduce((sum, hop) => sum + hop.peelAmount, 0), chain: chain, riskScore: Math.min(peelChainScore * 20, 100) // Max 100 }; } ``` ## Sanctions List Screening ### Global Sanctions Lists **Defy Live AML screens against:** 1. **OFAC (US Office of Foreign Assets Control)** - Specially Designated Nationals (SDN) List - Sectoral Sanctions Identifications (SSI) List - Updated daily 2. **EU Sanctions** - European Union consolidated list - Updated weekly 3. **UN Sanctions** - United Nations Security Council list - Updated as issued 4. **UK Sanctions** - HM Treasury's list - Post-Brexit independent list 5. **MASAK (Turkey)** - Turkish national sanctions list - Terrorist financing list 6. **FATF High-Risk Jurisdictions** - "Black list" (call for action) - "Grey list" (increased monitoring) ### Screening Implementation ```javascript import { DefyLiveAML } from '@defy/live-aml'; const liveAML = new DefyLiveAML({ apiKey: process.env.DEFY_API_KEY, sanctionsLists: ['OFAC', 'EU', 'UN', 'UK', 'MASAK'], updateFrequency: 'realtime' // Updates pushed in real-time }); async function screenTransaction(tx) { // Screen all addresses in transaction const addressesToScreen = [ tx.from, tx.to, ...tx.intermediateAddresses ]; const screeningResults = await liveAML.screenSanctions({ addresses: addressesToScreen, blockchain: tx.blockchain, includeIndirect: true, // Check addresses 2-3 hops away fuzzyMatching: true // Catch variations and typos }); if (screeningResults.hit) { // Sanctions hit detected await handleSanctionsHit({ transaction: tx, hitDetails: screeningResults, action: 'IMMEDIATE_REJECT' }); // File Suspicious Activity Report await fileSAR({ type: 'SANCTIONS_VIOLATION', transaction: tx, matchedLists: screeningResults.lists, matchedAddresses: screeningResults.matches }); return { allowed: false, reason: 'SANCTIONS_HIT', details: `Address ${screeningResults.matchedAddress} appears on ${screeningResults.lists.join(', ')}` }; } return { allowed: true, sanctionsScreen: 'PASS' }; } ``` ### False Positive Mitigation **Challenge:** Crypto address screening has higher false positive rates than traditional name-based screening due to address reuse and clustering. **Solution:** ```javascript async function advancedSanctionsScreening(address) { // Level 1: Direct address match const directMatch = await checkDirectMatch(address); if (directMatch.hit && directMatch.confidence > 0.99) { return { hit: true, confidence: 'HIGH', action: 'REJECT' }; } // Level 2: Cluster analysis const cluster = await getAddressCluster(address); const clusterMatches = await checkClusterSanctions(cluster); if (clusterMatches.hit && clusterMatches.clusterConfidence > 0.9) { return { hit: true, confidence: 'MEDIUM', action: 'MANUAL_REVIEW' }; } // Level 3: Entity resolution const entity = await resolveEntity(address); if (entity && entity.sanctioned) { return { hit: true, confidence: 'HIGH', action: 'REJECT' }; } // Level 4: Behavioral correlation const behavior = await analyzeBehavior(address); if (behavior.matchesSanctionedPattern && behavior.score > 0.85) { return { hit: true, confidence: 'LOW', action: 'ENHANCED_DUE_DILIGENCE' }; } return { hit: false }; } ``` ## Risk Scoring Model ### Unlimited Behavioral Signals **Transaction-Level Signals:** ```javascript const transactionSignals = { // Amount signals amount_unusually_high: tx.amount > user.avgTransactionAmount * 10, amount_just_below_reporting: tx.amount >= 9900 && tx.amount <= 10000, round_number: tx.amount % 1000 === 0, // Timing signals outside_normal_hours: getHour(tx.timestamp) < 6 || getHour(tx.timestamp) > 23, rapid_succession: timeSinceLastTx(user) < 60, // seconds burst_activity: countTxLast24h(user) > user.avgDaily * 5, // Destination signals new_address: !user.previouslyUsedAddresses.includes(tx.to), high_risk_jurisdiction: getRiskLevel(tx.toCountry) === 'HIGH', mixer_destination: isMixer(tx.to), exchange_to_exchange: isExchange(tx.from) && isExchange(tx.to), // User behavior signals first_transaction: user.transactionCount === 1, sudden_large_deposit: tx.isDeposit && tx.amount > user.totalBalance * 2, withdrawal_immediately_after_deposit: timeSinceDeposit(user) < 3600, // Network signals privacy_coin: tx.currency in ['XMR', 'ZEC', 'DASH'], tor_usage: tx.userIP && isTorExit(tx.userIP), vpn_usage: tx.userIP && isVPN(tx.userIP), // Historical signals past_suspicious_activity: user.pastSARCount > 0, chargebacks_history: user.chargebackCount > 2, multiple_accounts_same_ip: countAccountsWithIP(tx.userIP) > 3 }; ``` **Address-Level Signals:** ```javascript const addressSignals = { // Categorization is_exchange: addressCategory === 'exchange', is_mixer: addressCategory === 'mixer', is_gambling: addressCategory === 'gambling', is_darknet_market: addressCategory === 'darknet', is_ransomware: addressCategory === 'ransomware', is_scam: addressCategory === 'scam', // Activity patterns high_transaction_volume: address.tx24h > 1000, many_unique_counterparties: address.uniqueCounterparties > 500, short_lived_address: address.ageInDays < 7, // Risk indicators sanctions_proximity: minHopsToSanctionedAddress(address) <= 2, mixer_proximity: minHopsToMixer(address) <= 3, stolen_funds_taint: calculateTaint(address, 'stolen') > 0.1, // Cluster signals cluster_size: getClusterSize(address), cluster_has_sanctioned: clusterContainsSanctioned(address), cluster_activity_pattern: analyzeClusterPattern(address) }; ``` ### Machine Learning Risk Model **Ensemble Approach:** ```python import xgboost as xgb from sklearn.ensemble import RandomForestClassifier import numpy as np class AMLRiskScorer: def __init__(self): # Model 1: XGBoost for transaction patterns self.xgb_model = xgb.XGBClassifier( n_estimators=100, max_depth=6, learning_rate=0.1 ) # Model 2: Random Forest for address patterns self.rf_model = RandomForestClassifier( n_estimators=100, max_depth=10 ) # Model 3: Neural Network for complex patterns self.nn_model = build_neural_network() def calculate_risk_score(self, transaction, address_info, user_history): # Extract features tx_features = extract_transaction_features(transaction) address_features = extract_address_features(address_info) user_features = extract_user_features(user_history) # Get predictions from each model xgb_score = self.xgb_model.predict_proba([tx_features])[0][1] rf_score = self.rf_model.predict_proba([address_features])[0][1] nn_score = self.nn_model.predict([user_features])[0][0] # Ensemble: Weighted average risk_score = ( xgb_score * 0.4 + # Transaction patterns (40%) rf_score * 0.35 + # Address patterns (35%) nn_score * 0.25 # User behavior (25%) ) # Scale to 0-100 risk_score = risk_score * 100 # Apply rule-based adjustments if address_info['is_sanctioned']: risk_score = 100 # Override: Always maximum risk elif address_info['is_mixer']: risk_score = max(risk_score, 85) # Minimum 85 for mixer return { 'score': round(risk_score, 2), 'level': get_risk_level(risk_score), 'factors': get_risk_factors(transaction, address_info, user_history), 'model_scores': { 'transaction_model': round(xgb_score * 100, 2), 'address_model': round(rf_score * 100, 2), 'user_model': round(nn_score * 100, 2) } } def get_risk_level(score): if score < 30: return 'LOW' elif score < 70: return 'MEDIUM' else: return 'HIGH' ``` ### Risk Score Interpretation **Score Ranges:** - **0-30 (Low Risk - Green):** - Auto-approve - Standard monitoring - No additional due diligence - **31-70 (Medium Risk - Yellow):** - Manual review queue - Additional documentation may be requested - Enhanced monitoring for next 30 days - **71-84 (High Risk - Orange):** - Compliance team review required - Enhanced due diligence - Consider rejection or account limitation - **85-100 (Critical Risk - Red):** - Auto-reject - File SAR (Suspicious Activity Report) - Consider account termination - Report to authorities if required ## Automated SAR Filing ### When to File SAR **Regulatory Thresholds:** **USA (FinCEN):** - Transactions $5,000+ involving known/suspected money laundering - Transactions $25,000+ with no reasonable explanation - File within 30 days of detection **EU (5AMLD):** - Any transaction suspected of money laundering or terrorist financing - No minimum threshold - File "without delay" **Turkey (MASAK):** - Suspicious transactions regardless of amount - File within 10 business days ### Automated SAR Generation ```javascript async function automaticSARTrigger(transaction, riskAnalysis) { const shouldFileSAR = ( riskAnalysis.score >= 85 || // Critical risk riskAnalysis.factors.includes('SANCTIONS_HIT') || // Sanctions match riskAnalysis.factors.includes('MIXER_DIRECT') || // Direct mixer use (transaction.amount >= 5000 && riskAnalysis.score >= 70) // Large + high risk ); if (shouldFileSAR) { const sar = await generateSAR({ transaction: transaction, riskAnalysis: riskAnalysis, narrative: generateNarrative(transaction, riskAnalysis), supportingDocuments: await gatherEvidence(transaction) }); // Queue for compliance team review await queueSARForReview(sar); // Notify compliance officer await notifyComplianceOfficer({ type: 'SAR_GENERATED', sarId: sar.id, urgency: riskAnalysis.score >= 95 ? 'CRITICAL' : 'HIGH' }); return { sarFiled: true, sarId: sar.id }; } return { sarFiled: false }; } function generateNarrative(transaction, riskAnalysis) { const factors = riskAnalysis.factors; let narrative = `Suspicious transaction detected:\n\n`; narrative += `Transaction Details:\n`; narrative += `- Amount: ${transaction.amount} ${transaction.currency}\n`; narrative += `- Date: ${transaction.timestamp}\n`; narrative += `- Source: ${transaction.from}\n`; narrative += `- Destination: ${transaction.to}\n`; narrative += `- Blockchain: ${transaction.blockchain}\n\n`; narrative += `Risk Factors:\n`; factors.forEach((factor, index) => { narrative += `${index + 1}. ${formatFactor(factor)}\n`; }); narrative += `\nRisk Score: ${riskAnalysis.score}/100\n`; narrative += `Risk Level: ${riskAnalysis.level}\n`; return narrative; } ``` ## Defy Live AML Performance ### Real-World Metrics **Speed:** - Average analysis time: <2 seconds - 99.99% uptime SLA - 1M+ transactions processed daily **Accuracy:** - False positive rate: 8% (industry average: 40-60%) - Detection rate: 96% (missed only 4% of known suspicious activity) - Sanctions screening accuracy: 99.7% **Coverage:** - all popular blockchains supported - 1M+ known risky addresses - unlimited signals - 220 countries covered **Cost Savings:** - 60% reduction in manual review costs - 87% faster investigation cycles - 95% reduction in false positives ### Customer Success Story **Turkish Crypto Exchange - Case Study:** **Before Defy Live AML:** - Manual AML reviews: 2-3 days - False positive rate: 45% - 3 full-time compliance analysts - 1 missed SAR (regulatory warning) - User complaints: High (delayed withdrawals) **After Defy Live AML:** - Automated AML: <2 seconds - False positive rate: 7% - 1 compliance analyst (75% reduction) - 0 missed SARs (18 months) - User satisfaction: 94% (instant withdrawals) **ROI:** - Implementation cost: $15,000 - Monthly subscription: $3,000 - Annual savings: $180,000 (labor) + $50,000 (avoided penalties) - Net ROI Year 1: 540% ## Implementation Guide ### Integration Steps **Week 1: Setup & Configuration** ```javascript // Step 1: Install SDK npm install @defy/live-aml // Step 2: Initialize import { DefyLiveAML } from '@defy/live-aml'; const liveAML = new DefyLiveAML({ apiKey: process.env.DEFY_API_KEY, environment: 'production', blockchains: ['bitcoin', 'ethereum', 'binance-smart-chain'], riskThresholds: { autoApprove: 30, manualReview: 70, autoReject: 85 }, sanctionsLists: ['OFAC', 'EU', 'UN', 'MASAK'], webhookUrl: 'https://your-exchange.com/webhooks/aml' }); // Step 3: Configure rules await liveAML.configureRules({ mixer_detection: 'strict', hop_analysis_depth: 3, sanctions_screening: 'comprehensive', behavioral_analysis: 'enabled', auto_sar_filing: 'enabled' }); ``` **Week 2-3: Transaction Monitoring Integration** ```javascript // Integrate with your transaction flow app.post('/api/withdraw', async (req, res) => { const { userId, amount, currency, toAddress, blockchain } = req.body; // Run Live AML analysis const amlResult = await liveAML.analyzeTransaction({ userId: userId, transaction: { type: 'withdrawal', amount: amount, currency: currency, from: await getUserDepositAddress(userId, blockchain), to: toAddress, blockchain: blockchain }, userProfile: await getUserVerificationInfo(userId) }); // Handle result if (amlResult.approved) { // Green light: Process withdrawal const txHash = await processWithdrawal(req.body); res.json({ success: true, txHash, riskScore: amlResult.riskScore }); } else if (amlResult.requiresReview) { // Yellow: Queue for manual review const reviewId = await queueManualReview({ userId, transaction: req.body, amlAnalysis: amlResult }); res.json({ success: false, reason: 'PENDING_REVIEW', reviewId }); } else { // Red: Reject await logRejectedTransaction({ userId, transaction: req.body, amlAnalysis: amlResult }); res.status(400).json({ success: false, reason: 'AML_RISK_TOO_HIGH', riskScore: amlResult.riskScore }); } }); // Webhook handler for async updates app.post('/webhooks/aml', async (req, res) => { const event = req.body; switch (event.type) { case 'high_risk_detected': await notifyComplianceTeam(event); break; case 'sanctions_hit': await handleSanctionsAlert(event); break; case 'sar_filed': await updateTransactionStatus(event.transactionId, 'SAR_FILED'); break; } res.sendStatus(200); }); ``` **Week 4: Testing & Validation** ```javascript // Test with known scenarios const testCases = [ { name: 'Low risk legitimate transaction', toAddress: '0x1234... (verified exchange)', amount: 500, expectedRisk: 'LOW', expectedAction: 'APPROVE' }, { name: 'Mixer destination', toAddress: '0xTORNADO... (known mixer)', amount: 1000, expectedRisk: 'CRITICAL', expectedAction: 'REJECT' }, { name: 'Sanctioned address', toAddress: '0xSANCTIONED... (OFAC list)', amount: 100, expectedRisk: 'CRITICAL', expectedAction: 'REJECT' } ]; for (const testCase of testCases) { const result = await liveAML.analyzeTransaction(testCase); assert(result.riskLevel === testCase.expectedRisk); assert(result.action === testCase.expectedAction); console.log(`✓ Test passed: ${testCase.name}`); } ``` ## Conclusion: Real-Time AML is Essential In the fast-paced world of cryptocurrency, traditional batch-processing AML systems are insufficient. Criminals move funds in minutes, exploit cross-chain bridges, and use sophisticated obfuscation techniques. **Defy Live AML provides:** - **Real-time protection** (<2 seconds) - **Multi-chain coverage** (all popular blockchains) - **High accuracy** (96% detection, 8% false positives) - **Automated compliance** (SAR generation, regulatory reporting) - **Cost savings** (60% reduction in manual review) - **Better user experience** (instant approvals for legitimate users) ### Get Started **Pricing:** - Setup fee: $5,000 (one-time) - Monthly: $2,000 (up to 50,000 transactions) - Enterprise: Custom pricing (unlimited transactions) **Implementation:** - Week 1: Setup and configuration - Week 2-3: Integration and testing - Week 4: Production deployment - Ongoing: Monitoring and optimization **Contact:** - Email: info@getdefy.co - Demo: https://getdefy.co/live-aml-demo - Documentation: https://docs.getdefy.co/live-aml Protect your platform, satisfy regulators, and provide seamless user experience with Defy Live AML.