Crypto Compliance Monitoring: Ollama KYC and AML Automation Guide

# Minimum hardware specifications
# CPU: 8 cores, 2.4GHz
# RAM: 16GB (32GB recommended)
# Storage: 100GB SSD
# GPU: Optional (speeds up processing)

# Install Ollama on Ubuntu/Debian
curl -fsSL https://ollama.ai/install.sh | sh

# Verify installation
ollama --version

# Pull compliance-optimized model
ollama pull llama3.1:8b

# compliance_config.py
import ollama

class ComplianceConfig:
    def __init__(self):
        self.model = "llama3.1:8b"
        self.temperature = 0.1  # Low temperature for consistent outputs
        self.max_tokens = 500
        self.system_prompt = """
        You are a crypto compliance analyst. Analyze transactions for:
        - Money laundering patterns
        - Suspicious activity indicators  
        - Regulatory compliance violations
        Provide structured, factual responses only.
        """
    
    def get_client(self):
        return ollama.Client()

# kyc_analyzer.py
import ollama
import json
from datetime import datetime

class KYCAnalyzer:
    def __init__(self, config):
        self.client = config.get_client()
        self.model = config.model
        
    def analyze_identity_document(self, document_text, document_type):
        """
        Analyze identity documents for KYC compliance
        Returns structured verification results
        """
        
        prompt = f"""
        Analyze this {document_type} for KYC verification:
        
        Document Content: {document_text}
        
        Extract and verify:
        1. Full name (exactly as written)
        2. Date of birth (format: YYYY-MM-DD)
        3. Document number
        4. Expiration date
        5. Issuing authority
        6. Address (if present)
        
        Flag any inconsistencies or quality issues.
        Return results as JSON only.
        """
        
        response = self.client.generate(
            model=self.model,
            prompt=prompt,
            options={'temperature': 0.1}
        )
        
        try:
            return json.loads(response['response'])
        except json.JSONDecodeError:
            return {"error": "Failed to parse document"}
    
    def verify_customer_data(self, customer_info):
        """
        Cross-reference customer data against sanctions lists
        """
        
        prompt = f"""
        Review customer information for compliance risks:
        
        Name: {customer_info.get('name')}
        DOB: {customer_info.get('dob')}
        Address: {customer_info.get('address')}
        Nationality: {customer_info.get('nationality')}
        
        Check for:
        - Sanctions list matches (OFAC, UN, EU)
        - PEP (Politically Exposed Person) status
        - High-risk jurisdictions
        - Name variations that might indicate evasion
        
        Return risk assessment as JSON.
        """
        
        response = self.client.generate(
            model=self.model,
            prompt=prompt,
            options={'temperature': 0.1}
        )
        
        return response['response']

# Example KYC workflow
def process_new_customer(customer_data):
    """
    Complete KYC verification workflow
    """
    
    config = ComplianceConfig()
    kyc = KYCAnalyzer(config)
    
    # Step 1: Document verification
    doc_result = kyc.analyze_identity_document(
        customer_data['document_text'], 
        customer_data['document_type']
    )
    
    # Step 2: Risk assessment
    risk_result = kyc.verify_customer_data(customer_data)
    
    # Step 3: Decision logic
    if doc_result.get('verified') and 'high_risk' not in risk_result:
        return {"status": "approved", "confidence": 0.95}
    else:
        return {"status": "manual_review", "flags": doc_result.get('issues', [])}

# Usage example
customer = {
    'name': 'John Smith',
    'dob': '1985-03-15',
    'document_text': '[OCR extracted text]',
    'document_type': 'passport'
}

result = process_new_customer(customer)
print(f"KYC Result: {result}")

# aml_monitor.py
import ollama
import pandas as pd
from datetime import datetime, timedelta

class AMLMonitor:
    def __init__(self, config):
        self.client = config.get_client()
        self.model = config.model
        
        # AML risk indicators
        self.risk_patterns = {
            'structuring': 'Multiple transactions just under reporting thresholds',
            'rapid_movement': 'Funds moved between multiple wallets quickly',
            'mixing_services': 'Use of cryptocurrency mixing or tumbling services',
            'high_risk_exchanges': 'Transactions with sanctioned or high-risk exchanges',
            'unusual_volumes': 'Transaction amounts significantly above normal patterns'
        }
    
    def analyze_transaction_batch(self, transactions):
        """
        Analyze batch of transactions for suspicious patterns
        """
        
        # Prepare transaction summary
        tx_summary = self._create_transaction_summary(transactions)
        
        prompt = f"""
        Analyze these cryptocurrency transactions for AML compliance:
        
        Transaction Summary:
        {tx_summary}
        
        Known Risk Patterns:
        {json.dumps(self.risk_patterns, indent=2)}
        
        Identify:
        1. Suspicious transaction patterns
        2. Risk score (0-100)
        3. Specific indicators triggered
        4. Recommended actions
        
        Return analysis as structured JSON.
        """
        
        response = self.client.generate(
            model=self.model,
            prompt=prompt,
            options={'temperature': 0.1}
        )
        
        return response['response']
    
    def _create_transaction_summary(self, transactions):
        """
        Create formatted summary of transaction data
        """
        
        df = pd.DataFrame(transactions)
        
        summary = {
            'total_transactions': len(df),
            'total_volume': df['amount'].sum(),
            'unique_addresses': df['address'].nunique(),
            'time_span': f"{df['timestamp'].min()} to {df['timestamp'].max()}",
            'largest_transaction': df['amount'].max(),
            'average_transaction': df['amount'].mean()
        }
        
        return json.dumps(summary, indent=2)
    
    def real_time_screening(self, transaction):
        """
        Screen individual transaction in real-time
        """
        
        prompt = f"""
        Screen this crypto transaction for immediate risks:
        
        Amount: {transaction['amount']} 
        From: {transaction['from_address']}
        To: {transaction['to_address']}
        Currency: {transaction['currency']}
        Timestamp: {transaction['timestamp']}
        
        Quick assessment:
        - Block transaction? (yes/no)
        - Risk level: (low/medium/high)
        - Primary concern: (one sentence)
        
        JSON response only.
        """
        
        response = self.client.generate(
            model=self.model,
            prompt=prompt,
            options={'temperature': 0.0}  # Zero temperature for consistent decisions
        )
        
        return response['response']

# Real-time monitoring deployment
class ComplianceMonitor:
    def __init__(self):
        self.config = ComplianceConfig()
        self.aml = AMLMonitor(self.config)
        self.kyc = KYCAnalyzer(self.config)
        
    def process_transaction(self, tx_data):
        """
        Complete compliance check for incoming transaction
        """
        
        # Real-time AML screening
        aml_result = self.aml.real_time_screening(tx_data)
        
        # Log for batch analysis
        self._log_transaction(tx_data, aml_result)
        
        # Return immediate decision
        try:
            result = json.loads(aml_result)
            if result.get('block_transaction') == 'yes':
                return {"action": "block", "reason": result.get('primary_concern')}
            else:
                return {"action": "allow", "risk_level": result.get('risk_level')}
        except:
            return {"action": "manual_review", "reason": "Analysis failed"}
    
    def daily_batch_analysis(self):
        """
        Run comprehensive analysis on daily transaction batches
        """
        
        transactions = self._get_daily_transactions()
        batch_result = self.aml.analyze_transaction_batch(transactions)
        
        # Generate compliance report
        self._generate_report(batch_result)
        
        return batch_result

# dashboard.py
import streamlit as st
import plotly.graph_objects as go
from datetime import datetime

def create_compliance_dashboard():
    """
    Streamlit dashboard for compliance monitoring
    """
    
    st.set_page_config(page_title="Crypto Compliance Monitor", layout="wide")
    
    st.title("🔒 Crypto Compliance Monitoring Dashboard")
    
    # Real-time metrics
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric("Transactions Today", "2,847", "+12%")
    
    with col2:
        st.metric("Flagged Transactions", "23", "-5%")
    
    with col3:
        st.metric("False Positive Rate", "8%", "-2%")
    
    with col4:
        st.metric("Average Response Time", "0.3s", "-0.1s")
    
    # Transaction volume chart
    st.subheader("📊 Transaction Volume Analysis")
    
    # Placeholder for actual data
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=['2025-07-06', '2025-07-07', '2025-07-08', '2025-07-09', '2025-07-10'],
        y=[1200, 1350, 1180, 1420, 1500],
        mode='lines+markers',
        name='Daily Volume'
    ))
    
    fig.update_layout(title="5-Day Transaction Volume")
    st.plotly_chart(fig, use_container_width=True)
    
    # Recent alerts
    st.subheader("🚨 Recent Compliance Alerts")
    
    alerts_data = {
        'Timestamp': ['2025-07-10 14:30:15', '2025-07-10 13:45:22', '2025-07-10 12:15:08'],
        'Type': ['Structuring', 'High Risk Address', 'Unusual Volume'],
        'Risk Score': [85, 92, 78],
        'Status': ['Under Review', 'Escalated', 'Resolved']
    }
    
    st.dataframe(alerts_data, use_container_width=True)

if __name__ == "__main__":
    create_compliance_dashboard()

# docker-compose.yml
version: '3.8'
services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    environment:
      - OLLAMA_HOST=0.0.0.0
  
  compliance-api:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - ollama
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
  
  dashboard:
    build: ./dashboard
    ports:
      - "8501:8501"
    depends_on:
      - compliance-api

volumes:
  ollama_data:

# optimization.py
class OptimizedComplianceMonitor:
    def __init__(self):
        self.config = ComplianceConfig()
        self.cache = {}
        self.batch_size = 100
        
    def batch_process_transactions(self, transactions):
        """
        Process transactions in optimized batches
        """
        
        results = []
        
        for i in range(0, len(transactions), self.batch_size):
            batch = transactions[i:i + self.batch_size]
            batch_result = self._process_batch(batch)
            results.extend(batch_result)
            
            # Clear memory after each batch
            self._clear_cache()
        
        return results
    
    def _clear_cache(self):
        """
        Clear processing cache to manage memory
        """
        if len(self.cache) > 1000:
            self.cache.clear()

# travel_rule.py
def validate_travel_rule_compliance(transaction):
    """
    Ensure FATF Travel Rule compliance for transactions >$1000
    """
    
    if transaction['amount'] >= 1000:
        required_fields = [
            'originator_name',
            'originator_address', 
            'beneficiary_name',
            'beneficiary_address',
            'transaction_purpose'
        ]
        
        missing_fields = [field for field in required_fields 
                         if not transaction.get(field)]
        
        if missing_fields:
            return {
                "compliant": False,
                "missing_data": missing_fields,
                "action": "request_additional_info"
            }
    
    return {"compliant": True}

# jurisdiction_rules.py
JURISDICTION_RULES = {
    'US': {
        'reporting_threshold': 10000,
        'suspicious_amount': 3000,
        'required_licenses': ['MSB', 'FinCEN']
    },
    'EU': {
        'reporting_threshold': 10000,
        'suspicious_amount': 2000,
        'required_licenses': ['EMI', 'AISP']
    },
    'UK': {
        'reporting_threshold': 10000,
        'suspicious_amount': 1000,
        'required_licenses': ['FCA', 'PSR']
    }
}

def apply_jurisdiction_rules(transaction, jurisdiction):
    """
    Apply jurisdiction-specific compliance rules
    """
    
    rules = JURISDICTION_RULES.get(jurisdiction, {})
    
    if transaction['amount'] > rules.get('reporting_threshold', float('inf')):
        return {"action": "file_report", "authority": "financial_intelligence_unit"}
    
    if transaction['amount'] > rules.get('suspicious_amount', float('inf')):
        return {"action": "enhanced_monitoring", "duration": "30_days"}
    
    return {"action": "standard_processing"}

# Solution: Optimize model parameters
ollama run llama3.1:8b --num-ctx 2048 --num-predict 256

# Solution: Lower temperature setting
options = {
    'temperature': 0.0,  # Most deterministic
    'top_p': 0.1,        # Limit vocabulary 
    'repeat_penalty': 1.1
}

# Solution: Implement streaming responses
def stream_analysis(large_dataset):
    for chunk in batch_iterator(large_dataset, 50):
        yield process_chunk(chunk)

# Implement retry logic with exponential backoff
import time
import random

def robust_api_call(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise e
            time.sleep(2 ** attempt + random.uniform(0, 1))

# ml_enhancement.py
from sklearn.ensemble import RandomForestClassifier
import numpy as np

class HybridComplianceModel:
    def __init__(self):
        self.ollama_analyzer = AMLMonitor(ComplianceConfig())
        self.ml_model = RandomForestClassifier()
        
    def train_ml_component(self, historical_data, labels):
        """
        Train ML model on historical compliance decisions
        """
        
        features = self._extract_features(historical_data)
        self.ml_model.fit(features, labels)
        
    def hybrid_analysis(self, transaction):
        """
        Combine Ollama analysis with ML predictions
        """
        
        # Get Ollama assessment
        ollama_result = self.ollama_analyzer.real_time_screening(transaction)
        
        # Get ML prediction
        features = self._extract_features([transaction])
        ml_prediction = self.ml_model.predict_proba(features)[0]
        
        # Combine results
        combined_score = (
            self._parse_ollama_score(ollama_result) * 0.7 +
            ml_prediction[1] * 0.3
        )
        
        return {
            "risk_score": combined_score,
            "ollama_analysis": ollama_result,
            "ml_confidence": ml_prediction[1]
        }

# blockchain_monitor.py
from web3 import Web3

class BlockchainMonitor:
    def __init__(self, rpc_url):
        self.w3 = Web3(Web3.HTTPProvider(rpc_url))
        self.compliance = ComplianceMonitor()
        
    def monitor_real_time_transactions(self):
        """
        Monitor blockchain for new transactions in real-time
        """
        
        def handle_event(event):
            tx_hash = event['transactionHash']
            tx = self.w3.eth.get_transaction(tx_hash)
            
            # Convert to compliance format
            transaction_data = {
                'amount': self.w3.from_wei(tx['value'], 'ether'),
                'from_address': tx['from'],
                'to_address': tx['to'],
                'timestamp': datetime.now().isoformat(),
                'currency': 'ETH'
            }
            
            # Run compliance check
            result = self.compliance.process_transaction(transaction_data)
            
            if result['action'] == 'block':
                self._alert_compliance_team(transaction_data, result)
        
        # Set up event filter
        event_filter = self.w3.eth.filter('latest')
        event_filter.watch(handle_event)