How I Automated KYC/AML Checks for a Crypto Exchange (Cut 80% Processing Time)

A mid-sized crypto exchange was hemorrhaging money on manual compliance. Their KYC team was processing 200+ applications daily, each taking 45 minutes of manual work. Compliance delays meant frustrated customers and lost revenue.

The challenge: European crypto regulations require strict KYC/AML verification, but manual processes couldn't keep up with demand.

The result: Automated 80% of the verification pipeline, reducing average processing time from 45 minutes to 9 minutes while improving compliance accuracy.

Here's exactly how I built it.

The Manual Process (That Was Bleeding Money)

Before automation, every customer verification required:

Document collection: Upload ID, proof of address, bank statements
Manual review: Compliance officer checks document authenticity
Risk assessment: Manual scoring based on geography, transaction patterns, politically exposed persons (PEP) lists
AML screening: Cross-reference against sanctions lists, adverse media
Decision making: Approve, reject, or flag for further review
Reporting: Generate compliance reports for regulators

Time per application: 45 minutes average Daily volume: 200+ applications Team cost: 6 full-time compliance officers at €60K/year each Customer frustration: 24-48 hour approval times

Annual cost of manual KYC: €360K in salaries + €200K in lost customers due to delays = €560K yearly

The Automated Solution Architecture

Stage 1: Intelligent Document Processing

Problem: Manual document verification was the biggest bottleneck.

Solution: AI-powered document analysis using computer vision + OCR.

Tech stack:

Google Cloud Document AI for OCR and data extraction
Custom TensorFlow model trained on ID document patterns
Image processing pipeline for quality checks and fraud detection

Implementation:

// Document verification types
interface DocumentVerificationResult {
  status: 'approved'

// Risk assessment types interface CustomerRiskData { customerId: string; fullName: string; dateOfBirth: string; country: string; nationality: string; occupation?: string; estimatedIncome?: number; } interface RiskFactors { geographic: number; pepStatus: number; sanctions: number; adverseMedia: number; transactionPatterns: number; } interface RiskAssessmentResult { overallScore: number; riskLevel: 'low' | 'medium' | 'high' | 'critical'; factors: RiskFactors; recommendation: 'approve' | 'review' | 'reject'; reasoning: string[]; } // Risk scoring engine async function calculateRiskScore(customerData: CustomerRiskData): Promise<RiskAssessmentResult> { // Parallel execution of risk checks const [ geographicRisk, pepStatus, sanctionsCheck, adverseMediaCheck, transactionPatterns ] = await Promise.all([ assessCountryRisk(customerData.country), checkPepLists(customerData.fullName, customerData.dateOfBirth), checkSanctionsLists(customerData), scanAdverseMedia(customerData.fullName), analyzeTransactionHistory(customerData.customerId) ]); const riskFactors: RiskFactors = { geographic: geographicRisk, pepStatus: pepStatus, sanctions: sanctionsCheck, adverseMedia: adverseMediaCheck, transactionPatterns: transactionPatterns }; // Weighted risk calculation with business rules const riskWeights = { geographic: 0.2, pepStatus: 0.3, sanctions: 0.4, adverseMedia: 0.1, transactionPatterns: 0.2 }; const totalRisk = Object.entries(riskFactors).reduce( (total, [factor, score]) => total + score * riskWeights[factor as keyof RiskFactors], 0 ); return { overallScore: totalRisk, riskLevel: categorizeRisk(totalRisk), factors: riskFactors, recommendation: generateRecommendation(totalRisk, riskFactors), reasoning: generateReasoningExplanation(riskFactors) }; } // Business logic for risk categorization function categorizeRisk(score: number): RiskAssessmentResult['riskLevel'] { if (score <= 0.3) return 'low'; if (score <= 0.7) return 'medium'; if (score <= 0.85) return 'high'; return 'critical'; } // Compliance-aware recommendation engine function generateRecommendation( overallScore: number, factors: RiskFactors ): RiskAssessmentResult['recommendation'] { // Hard rules for sanctions and PEP if (factors.sanctions > 0.8) return 'reject'; if (factors.pepStatus > 0.9) return 'review'; // Score-based decision if (overallScore <= 0.3) return 'approve'; if (overallScore <= 0.85) return 'review'; return 'reject'; } // External API integrations async function checkSanctionsLists(customerData: CustomerRiskData): Promise<number> { try { const response = await fetch('/api/sanctions/check', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ name: customerData.fullName, dateOfBirth: customerData.dateOfBirth, country: customerData.country }) }); const result = await response.json(); return result.riskScore; } catch (error) { console.error('Sanctions check failed:', error); // Return high risk score if check fails for compliance safety return 0.9; } }

// Decision engine types interface KYCDecisionResult { decision: 'auto_approved' | 'approved_with_monitoring' | 'human_review' | 'auto_rejected'; confidence: number; reasoning: string[]; nextActions: string[]; reviewRequired: boolean; complianceRecord: ComplianceRecord; } interface ComplianceRecord { decisionId: string; timestamp: string; riskFactors: RiskFactors; documentVerification: DocumentVerificationResult; decisionReasoning: string[]; reviewerRequired?: string; } // Main decision engine async function makeKYCDecision( documentScore: DocumentVerificationResult, riskScore: RiskAssessmentResult, customerData: CustomerRiskData ): Promise<KYCDecisionResult> { // Validate inputs if (!documentScore.extractedData || documentScore.confidence < 0.5) { return createRejectionDecision('Insufficient document verification'); } // Calculate combined confidence const combinedConfidence = (documentScore.confidence + (1 - riskScore.overallScore)) / 2; // Decision matrix based on risk and confidence let decision: KYCDecisionResult['decision']; let reasoning: string[] = []; let reviewRequired = false; if (riskScore.overallScore <= 0.3 && documentScore.confidence > 0.9) { decision = 'auto_approved'; reasoning.push('Low risk profile with high document confidence'); await executeAutoApproval(customerData); } else if (riskScore.overallScore <= 0.7 && combinedConfidence > 0.8) { decision = 'approved_with_monitoring'; reasoning.push('Moderate risk requiring enhanced monitoring'); await executeApprovalWithMonitoring(customerData, riskScore); } else if (riskScore.overallScore <= 0.85) { decision = 'human_review'; reasoning.push('Risk level requires human assessment'); reviewRequired = true; await flagForHumanReview(customerData, riskScore.factors); } else { decision = 'auto_rejected'; reasoning.push('Risk level exceeds acceptable thresholds'); await executeAutoRejection(customerData, riskScore.factors); } // Generate compliance record const complianceRecord: ComplianceRecord = { decisionId: generateDecisionId(), timestamp: new Date().toISOString(), riskFactors: riskScore.factors, documentVerification: documentScore, decisionReasoning: reasoning }; return { decision, confidence: combinedConfidence, reasoning, nextActions: generateNextActions(decision, riskScore), reviewRequired, complianceRecord }; } // Auto-approval workflow async function executeAutoApproval(customerData: CustomerRiskData): Promise<void> { try { // Parallel execution of approval tasks await Promise.all([ generateComplianceRecord(customerData, 'auto_approved'), sendWelcomeEmail(customerData), enableTradingPermissions(customerData.customerId), logDecisionForAudit(customerData, 'auto_approved'), notifyCustomerSuccess(customerData) ]); console.log(`Auto-approved customer: ${customerData.customerId}`); } catch (error) { console.error('Auto-approval failed:', error); // Fallback to manual review if automation fails await flagForHumanReview(customerData, { error: 'automation_failure' }); } } // Enhanced monitoring setup async function executeApprovalWithMonitoring( customerData: CustomerRiskData, riskScore: RiskAssessmentResult ): Promise<void> { await Promise.all([ enableTradingPermissions(customerData.customerId), setupEnhancedMonitoring(customerData.customerId, riskScore.factors), schedulePeriodicReview(customerData.customerId, '90_days'), logDecisionForAudit(customerData, 'approved_with_monitoring'), sendConditionalWelcomeEmail(customerData) ]); } // Human review flagging with context async function flagForHumanReview( customerData: CustomerRiskData, riskFactors: any ): Promise<void> { const reviewCase = { customerId: customerData.customerId, priority: calculateReviewPriority(riskFactors), context: generateReviewContext(customerData, riskFactors), suggestedActions: generateSuggestedActions(riskFactors), deadline: calculateReviewDeadline(riskFactors) }; await Promise.all([ addToReviewQueue(reviewCase), notifyComplianceTeam(reviewCase), logDecisionForAudit(customerData, 'flagged_for_review'), sendPendingNotificationToCustomer(customerData) ]); } // Error handling and fallback mechanisms class KYCDecisionError extends Error { constructor( message: string, public readonly customerData: CustomerRiskData, public readonly stage: string ) { super(message); this.name = 'KYCDecisionError'; } } function createRejectionDecision(reason: string): KYCDecisionResult { return { decision: 'auto_rejected', confidence: 1.0, reasoning: [reason], nextActions: ['Customer can appeal decision', 'Provide additional documentation'], reviewRequired: false, complianceRecord: { decisionId: generateDecisionId(), timestamp: new Date().toISOString(), riskFactors: {} as RiskFactors, documentVerification: {} as DocumentVerificationResult, decisionReasoning: [reason] } }; }

The Manual Process (That Was Bleeding Money)

The Automated Solution Architecture

Stage 1: Intelligent Document Processing

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Stage 2: Intelligent Risk Scoring

Stage 3: Automated Decision Engine

Stage 4: Real-Time Compliance Reporting

Implementation Timeline & Challenges

Week 1-2: Infrastructure Setup

Week 3-4: Risk Scoring Engine

Week 5-6: Decision Engine & Integration

Week 7-8: Testing & Compliance Review

Results & ROI

Processing Speed Improvement

Cost Savings

Accuracy & Compliance

Customer Experience

Lessons Learned & What I'd Do Differently

What Worked Well

What I'd Improve

Regulatory Considerations

Technical Architecture Deep Dive

Data Flow

Security Measures

Scalability

Industry Impact & Compliance

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