Provenance Blockchain Analysis: Ollama $12B Private Credit Securities Tracking

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

# Pull required models for financial analysis
ollama pull llama2:13b
ollama pull codellama:34b

# Verify installation
ollama list

import requests
import json
from datetime import datetime

class ProvenanceAnalyzer:
    def __init__(self, blockchain_endpoint, ollama_endpoint):
        self.blockchain_url = blockchain_endpoint
        self.ollama_url = ollama_endpoint
        
    def fetch_transaction_data(self, security_id):
        """Retrieve complete transaction history for a security"""
        endpoint = f"{self.blockchain_url}/api/v1/securities/{security_id}/transactions"
        
        try:
            response = requests.get(endpoint)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            print(f"Blockchain connection failed: {e}")
            return None
            
    def analyze_with_ollama(self, transaction_data):
        """Send transaction data to Ollama for analysis"""
        prompt = f"""
        Analyze this private credit security transaction chain:
        {json.dumps(transaction_data, indent=2)}
        
        Identify:
        1. Ownership transfer patterns
        2. Risk indicators
        3. Compliance violations
        4. Fraud signals
        """
        
        payload = {
            "model": "llama2:13b",
            "prompt": prompt,
            "stream": False
        }
        
        response = requests.post(f"{self.ollama_url}/api/generate", json=payload)
        return response.json()['response']

# Initialize analyzer
analyzer = ProvenanceAnalyzer(
    blockchain_endpoint="https://your-blockchain-node.com",
    ollama_endpoint="http://localhost:11434"
)

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.19;

contract PrivateCreditProvenance {
    struct SecurityRecord {
        string assetId;
        address currentOwner;
        uint256 value;
        uint256 timestamp;
        string metadata;
        bytes32 previousRecordHash;
    }
    
    mapping(string => SecurityRecord[]) public assetHistory;
    mapping(string => address) public assetOwners;
    
    event AssetTransferred(
        string indexed assetId,
        address indexed from,
        address indexed to,
        uint256 value,
        uint256 timestamp
    );
    
    function recordTransfer(
        string memory _assetId,
        address _newOwner,
        uint256 _value,
        string memory _metadata
    ) public {
        // Verify current ownership
        require(
            assetOwners[_assetId] == msg.sender || assetOwners[_assetId] == address(0),
            "Unauthorized transfer attempt"
        );
        
        // Calculate previous record hash for chain integrity
        bytes32 prevHash = keccak256(
            abi.encodePacked(getLatestRecord(_assetId))
        );
        
        // Create new provenance record
        SecurityRecord memory newRecord = SecurityRecord({
            assetId: _assetId,
            currentOwner: _newOwner,
            value: _value,
            timestamp: block.timestamp,
            metadata: _metadata,
            previousRecordHash: prevHash
        });
        
        assetHistory[_assetId].push(newRecord);
        assetOwners[_assetId] = _newOwner;
        
        emit AssetTransferred(_assetId, msg.sender, _newOwner, _value, block.timestamp);
    }
    
    function getLatestRecord(string memory _assetId) 
        public 
        view 
        returns (SecurityRecord memory) {
        require(assetHistory[_assetId].length > 0, "No records found");
        return assetHistory[_assetId][assetHistory[_assetId].length - 1];
    }
    
    function verifyChainIntegrity(string memory _assetId) 
        public 
        view 
        returns (bool) {
        SecurityRecord[] memory records = assetHistory[_assetId];
        
        for (uint i = 1; i < records.length; i++) {
            bytes32 expectedHash = keccak256(abi.encodePacked(records[i-1]));
            if (records[i].previousRecordHash != expectedHash) {
                return false;
            }
        }
        return true;
    }
}

import asyncio
import websockets
import json
from datetime import datetime, timedelta

class ProvenanceMonitor:
    def __init__(self, websocket_url, analyzer):
        self.ws_url = websocket_url
        self.analyzer = analyzer
        self.alert_thresholds = {
            'rapid_transfers': 5,  # Max transfers per hour
            'value_spike': 0.5,   # 50% value increase threshold
            'ownership_cycles': 3  # Max ownership cycles
        }
        
    async def monitor_transactions(self):
        """Monitor blockchain for real-time transaction events"""
        async with websockets.connect(self.ws_url) as websocket:
            # Subscribe to transfer events
            subscription = {
                "jsonrpc": "2.0",
                "method": "eth_subscribe",
                "params": ["logs", {
                    "topics": [
                        "0x..." # AssetTransferred event signature
                    ]
                }],
                "id": 1
            }
            
            await websocket.send(json.dumps(subscription))
            
            while True:
                response = await websocket.recv()
                event_data = json.loads(response)
                
                if 'params' in event_data:
                    await self.process_transfer_event(event_data['params']['result'])
                    
    async def process_transfer_event(self, event_log):
        """Analyze individual transfer events for risk indicators"""
        # Decode event data
        asset_id = self.decode_asset_id(event_log['data'])
        from_address = event_log['topics'][1]
        to_address = event_log['topics'][2]
        
        # Fetch complete asset history
        history = self.analyzer.fetch_transaction_data(asset_id)
        
        # Check for risk patterns
        alerts = []
        
        # Rapid transfer detection
        recent_transfers = self.count_recent_transfers(history, hours=1)
        if recent_transfers > self.alert_thresholds['rapid_transfers']:
            alerts.append(f"Rapid transfer alert: {recent_transfers} transfers in 1 hour")
            
        # Value manipulation detection
        if self.detect_value_manipulation(history):
            alerts.append("Potential value manipulation detected")
            
        # Ownership cycle detection
        if self.detect_ownership_cycles(history):
            alerts.append("Circular ownership pattern detected")
            
        if alerts:
            await self.send_alerts(asset_id, alerts)
            
    def count_recent_transfers(self, history, hours):
        """Count transfers within specified time window"""
        cutoff_time = datetime.now() - timedelta(hours=hours)
        recent_count = 0
        
        for transaction in history['transactions']:
            tx_time = datetime.fromtimestamp(transaction['timestamp'])
            if tx_time > cutoff_time:
                recent_count += 1
                
        return recent_count
        
    def detect_value_manipulation(self, history):
        """Identify unusual value changes"""
        transactions = history['transactions']
        if len(transactions) < 2:
            return False
            
        # Check for sudden value spikes
        for i in range(1, len(transactions)):
            prev_value = transactions[i-1]['value']
            curr_value = transactions[i]['value']
            
            if prev_value > 0:
                change_ratio = abs(curr_value - prev_value) / prev_value
                if change_ratio > self.alert_thresholds['value_spike']:
                    return True
                    
        return False
        
    def detect_ownership_cycles(self, history):
        """Detect circular ownership patterns"""
        owners = [tx['to_address'] for tx in history['transactions']]
        
        # Check for repeated ownership patterns
        for i, owner in enumerate(owners):
            future_occurrences = owners[i+1:].count(owner)
            if future_occurrences >= self.alert_thresholds['ownership_cycles']:
                return True
                
        return False
        
    async def send_alerts(self, asset_id, alerts):
        """Send alerts to monitoring dashboard"""
        alert_data = {
            'asset_id': asset_id,
            'timestamp': datetime.now().isoformat(),
            'severity': 'HIGH',
            'alerts': alerts
        }
        
        # Send to dashboard API
        response = requests.post(
            'https://your-dashboard.com/api/alerts',
            json=alert_data
        )
        
        print(f"Alert sent for asset {asset_id}: {alerts}")

# Start monitoring
monitor = ProvenanceMonitor("wss://blockchain-node.com/ws", analyzer)
asyncio.run(monitor.monitor_transactions())

class NaturalLanguageAnalyzer:
    def __init__(self, analyzer):
        self.analyzer = analyzer
        
    def process_query(self, user_query):
        """Convert natural language to blockchain query"""
        
        # Send query to Ollama for interpretation
        interpretation_prompt = f"""
        Convert this business question into specific blockchain data queries:
        "{user_query}"
        
        Available data fields:
        - asset_id: Security identifier
        - owner_history: List of previous owners
        - transaction_values: Transfer amounts
        - timestamps: Transaction times
        - metadata: Additional transaction details
        
        Return structured query parameters in JSON format.
        """
        
        payload = {
            "model": "codellama:34b",
            "prompt": interpretation_prompt,
            "stream": False
        }
        
        response = requests.post("http://localhost:11434/api/generate", json=payload)
        query_params = json.loads(response.json()['response'])
        
        # Execute blockchain queries
        results = self.execute_queries(query_params)
        
        # Generate human-readable report
        return self.generate_report(user_query, results)
        
    def execute_queries(self, query_params):
        """Execute structured queries against blockchain"""
        results = {}
        
        for query_type, params in query_params.items():
            if query_type == "ownership_analysis":
                results[query_type] = self.analyze_ownership_patterns(params)
            elif query_type == "value_analysis":
                results[query_type] = self.analyze_value_trends(params)
            elif query_type == "compliance_check":
                results[query_type] = self.check_compliance_status(params)
                
        return results
        
    def generate_report(self, original_query, analysis_results):
        """Generate business-friendly report"""
        
        report_prompt = f"""
        Original question: "{original_query}"
        
        Analysis results:
        {json.dumps(analysis_results, indent=2)}
        
        Generate a clear, non-technical summary that answers the original question.
        Include specific numbers, dates, and actionable insights.
        """
        
        payload = {
            "model": "llama2:13b",
            "prompt": report_prompt,
            "stream": False
        }
        
        response = requests.post("http://localhost:11434/api/generate", json=payload)
        return response.json()['response']

# Example usage
nl_analyzer = NaturalLanguageAnalyzer(analyzer)

# Business user asks plain English question
query = "Which private credit securities had ownership changes in the last 30 days worth more than $1 million?"
report = nl_analyzer.process_query(query)
print(report)

class ComplianceReporter:
    def __init__(self, analyzer):
        self.analyzer = analyzer
        self.regulations = {
            'sox': self.sox_compliance_check,
            'aml': self.aml_compliance_check,
            'kyc': self.kyc_compliance_check
        }
        
    def generate_monthly_report(self, regulation_type):
        """Generate comprehensive compliance report"""
        
        # Gather all transactions from last month
        end_date = datetime.now()
        start_date = end_date - timedelta(days=30)
        
        monthly_data = self.fetch_monthly_transactions(start_date, end_date)
        
        # Run compliance checks
        compliance_results = self.regulations[regulation_type](monthly_data)
        
        # Generate report with Ollama
        report = self.create_formatted_report(regulation_type, compliance_results)
        
        return report
        
    def sox_compliance_check(self, transaction_data):
        """Sarbanes-Oxley compliance verification"""
        violations = []
        
        for transaction in transaction_data:
            # Check for proper authorization
            if not transaction.get('authorization_signature'):
                violations.append({
                    'asset_id': transaction['asset_id'],
                    'violation': 'Missing authorization signature',
                    'severity': 'HIGH'
                })
                
            # Check for dual approval on high-value transactions
            if transaction['value'] > 1000000:  # $1M threshold
                if len(transaction.get('approvers', [])) < 2:
                    violations.append({
                        'asset_id': transaction['asset_id'],
                        'violation': 'High-value transaction lacks dual approval',
                        'severity': 'CRITICAL'
                    })
                    
        return {
            'total_transactions': len(transaction_data),
            'violations': violations,
            'compliance_rate': (len(transaction_data) - len(violations)) / len(transaction_data) * 100
        }
        
    def aml_compliance_check(self, transaction_data):
        """Anti-Money Laundering compliance verification"""
        suspicious_patterns = []
        
        # Group transactions by owner
        owner_transactions = {}
        for tx in transaction_data:
            owner = tx['current_owner']
            if owner not in owner_transactions:
                owner_transactions[owner] = []
            owner_transactions[owner].append(tx)
            
        # Check for structuring patterns
        for owner, transactions in owner_transactions.items():
            daily_amounts = {}
            
            for tx in transactions:
                date = datetime.fromtimestamp(tx['timestamp']).date()
                if date not in daily_amounts:
                    daily_amounts[date] = 0
                daily_amounts[date] += tx['value']
                
            # Flag amounts just under reporting thresholds
            for date, amount in daily_amounts.items():
                if 9500 <= amount <= 9999:  # Just under $10k threshold
                    suspicious_patterns.append({
                        'owner': owner,
                        'date': date.isoformat(),
                        'amount': amount,
                        'pattern': 'Potential structuring'
                    })
                    
        return {
            'total_owners': len(owner_transactions),
            'suspicious_patterns': suspicious_patterns,
            'risk_score': len(suspicious_patterns) / len(owner_transactions) * 100
        }
        
    def create_formatted_report(self, regulation_type, results):
        """Create professional compliance report"""
        
        report_prompt = f"""
        Create a professional compliance report for {regulation_type.upper()} regulations.
        
        Data summary:
        {json.dumps(results, indent=2)}
        
        Include:
        1. Executive summary with key findings
        2. Detailed violation analysis
        3. Risk assessment
        4. Recommended actions
        5. Compliance metrics and trends
        
        Format as a formal regulatory report.
        """
        
        payload = {
            "model": "llama2:13b",
            "prompt": report_prompt,
            "stream": False
        }
        
        response = requests.post("http://localhost:11434/api/generate", json=payload)
        
        # Add metadata and formatting
        report_content = response.json()['response']
        
        formatted_report = f"""
# {regulation_type.upper()} Compliance Report
**Report Period:** {datetime.now().strftime('%B %Y')}
**Generated:** {datetime.now().isoformat()}
**Status:** {'COMPLIANT' if len(results.get('violations', [])) == 0 else 'NON-COMPLIANT'}

{report_content}

---
*This report was automatically generated using blockchain provenance analysis.*
        """
        
        return formatted_report

# Generate compliance reports
compliance_reporter = ComplianceReporter(analyzer)
sox_report = compliance_reporter.generate_monthly_report('sox')
print(sox_report)

class OptimizedProvenanceStore:
    def __init__(self, redis_client, postgres_client):
        self.cache = redis_client
        self.db = postgres_client
        
    def cache_frequently_accessed_data(self):
        """Cache hot data for faster queries"""
        
        # Cache recent transactions (last 7 days)
        recent_query = """
        SELECT asset_id, transaction_data 
        FROM provenance_records 
        WHERE timestamp > NOW() - INTERVAL '7 days'
        ORDER BY timestamp DESC
        """
        
        recent_transactions = self.db.fetch_all(recent_query)
        
        for record in recent_transactions:
            cache_key = f"recent:{record['asset_id']}"
            self.cache.setex(
                cache_key, 
                3600,  # 1 hour TTL
                json.dumps(record['transaction_data'])
            )
            
    def batch_process_analytics(self, asset_ids):
        """Process multiple assets efficiently"""
        
        # Batch blockchain queries
        batch_size = 100
        results = {}
        
        for i in range(0, len(asset_ids), batch_size):
            batch = asset_ids[i:i + batch_size]
            
            # Single query for entire batch
            batch_query = """
            SELECT asset_id, array_agg(transaction_data ORDER BY timestamp) as history
            FROM provenance_records 
            WHERE asset_id = ANY(%s)
            GROUP BY asset_id
            """
            
            batch_results = self.db.fetch_all(batch_query, (batch,))
            
            for result in batch_results:
                results[result['asset_id']] = result['history']
                
        return results
        
    def create_analytics_indexes(self):
        """Create database indexes for faster queries"""
        
        indexes = [
            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_provenance_timestamp ON provenance_records(timestamp);",
            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_provenance_asset_id ON provenance_records(asset_id);",
            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_provenance_owner ON provenance_records((transaction_data->>'current_owner'));",
            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_provenance_value ON provenance_records(((transaction_data->>'value')::numeric));"
        ]
        
        for index_sql in indexes:
            self.db.execute(index_sql)
            
        print("Analytics indexes created successfully")

# Initialize optimized storage
optimized_store = OptimizedProvenanceStore(redis_client, postgres_client)
optimized_store.create_analytics_indexes()
optimized_store.cache_frequently_accessed_data()

class DistributedAnalyzer:
    def __init__(self, cluster_nodes):
        self.nodes = cluster_nodes
        self.load_balancer = self.setup_load_balancer()
        
    def setup_load_balancer(self):
        """Configure intelligent load balancing"""
        return {
            'strategy': 'least_connections',
            'health_check_interval': 30,
            'failover_threshold': 3
        }
        
    def distribute_analysis_workload(self, asset_list):
        """Distribute analysis across cluster nodes"""
        
        # Partition assets by hash for consistent distribution
        partitioned_assets = self.partition_assets(asset_list)
        
        # Submit jobs to different nodes
        analysis_jobs = []
        
        for node_id, assets in partitioned_assets.items():
            job = {
                'node_id': node_id,
                'assets': assets,
                'analysis_type': 'full_provenance',
                'priority': 'normal'
            }
            
            analysis_jobs.append(self.submit_to_node(node_id, job))
            
        # Collect results
        return self.collect_distributed_results(analysis_jobs)
        
    def partition_assets(self, asset_list):
        """Partition assets across available nodes"""
        partitions = {i: [] for i in range(len(self.nodes))}
        
        for asset_id in asset_list:
            # Use consistent hashing for partition assignment
            partition_id = hash(asset_id) % len(self.nodes)
            partitions[partition_id].append(asset_id)
            
        return partitions
        
    async def submit_to_node(self, node_id, job):
        """Submit analysis job to specific node"""
        node_endpoint = self.nodes[node_id]['endpoint']
        
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{node_endpoint}/api/analyze",
                json=job
            ) as response:
                return await response.json()
                
    def collect_distributed_results(self, job_futures):
        """Aggregate results from all nodes"""
        aggregated_results = {}
        
        for future in asyncio.as_completed(job_futures):
            node_results = await future
            aggregated_results.update(node_results)
            
        return aggregated_results

# Configure distributed analysis
cluster_nodes = [
    {'id': 0, 'endpoint': 'http://analyzer-node-1.internal:8080'},
    {'id': 1, 'endpoint': 'http://analyzer-node-2.internal:8080'},
    {'id': 2, 'endpoint': 'http://analyzer-node-3.internal:8080'}
]

distributed_analyzer = DistributedAnalyzer(cluster_nodes)

class SecureProvenanceAccess:
    def __init__(self, vault_client, auth_service):
        self.vault = vault_client
        self.auth = auth_service
        
    def authenticate_request(self, request):
        """Multi-factor authentication for sensitive operations"""
        
        # Verify JWT token
        token = request.headers.get('Authorization', '').replace('Bearer ', '')
        user_claims = self.auth.verify_token(token)
        
        if not user_claims:
            raise UnauthorizedException("Invalid authentication token")
            
        # Check user permissions
        required_permissions = self.get_required_permissions(request.endpoint)
        user_permissions = user_claims.get('permissions', [])
        
        if not all(perm in user_permissions for perm in required_permissions):
            raise ForbiddenException("Insufficient permissions")
            
        # Require MFA for high-risk operations
        if self.is_high_risk_operation(request):
            mfa_token = request.headers.get('X-MFA-Token')
            if not self.auth.verify_mfa(user_claims['user_id'], mfa_token):
                raise MFARequiredException("MFA verification required")
                
        return user_claims
        
    def encrypt_sensitive_data(self, data, data_classification):
        """Apply encryption based on data sensitivity"""
        
        encryption_keys = {
            'public': None,  # No encryption for public data
            'internal': self.vault.get_secret('encryption/internal')['key'],
            'confidential': self.vault.get_secret('encryption/confidential')['key'],
            'restricted': self.vault.get_secret('encryption/restricted')['key']
        }
        
        if data_classification == 'public':
            return data
            
        encryption_key = encryption_keys[data_classification]
        
        # Use AES-256-GCM for symmetric encryption
        from cryptography.fernet import Fernet
        cipher = Fernet(encryption_key)
        
        encrypted_data = cipher.encrypt(json.dumps(data).encode())
        
        return {
            'encrypted': True,
            'classification': data_classification,
            'data': encrypted_data.decode(),
            'timestamp': datetime.now().isoformat()
        }
        
    def audit_access_patterns(self, user_id, accessed_assets):
        """Monitor for unusual access patterns"""
        
        # Get user's historical access patterns
        historical_access = self.get_user_access_history(user_id, days=30)
        
        # Analyze current access against patterns
        unusual_patterns = []
        
        # Check for time-based anomalies
        current_hour = datetime.now().hour
        typical_hours = [access['hour'] for access in historical_access]
        
        if current_hour not in typical_hours:
            unusual_patterns.append({
                'type': 'unusual_time',
                'description': f'Access at {current_hour}:00 is outside normal hours'
            })
            
        # Check for volume anomalies
        typical_daily_access = len(historical_access) / 30
        current_access_count = len(accessed_assets)
        
        if current_access_count > typical_daily_access * 3:
            unusual_patterns.append({
                'type': 'high_volume',
                'description': f'Accessing {current_access_count} assets (3x normal volume)'
            })
            
        # Check for asset type anomalies
        historical_asset_types = set(access['asset_type'] for access in historical_access)
        current_asset_types = set(asset['type'] for asset in accessed_assets)
        
        new_asset_types = current_asset_types - historical_asset_types
        if new_asset_types:
            unusual_patterns.append({
                'type': 'new_asset_types',
                'description': f'First time accessing: {", ".join(new_asset_types)}'
            })
            
        if unusual_patterns:
            self.trigger_security_review(user_id, unusual_patterns)
            
        return unusual_patterns

# Implement secure access controls
secure_access = SecureProvenanceAccess(vault_client, auth_service)

class EnterpriseIntegration:
    def __init__(self):
        self.integrations = {
            'core_banking': self.connect_core_banking,
            'risk_management': self.connect_risk_system,
            'regulatory_reporting': self.connect_reporting_system,
            'portfolio_management': self.connect_portfolio_system
        }
        
    def connect_core_banking(self, bank_system_config):
        """Integrate with core banking systems"""
        
        class CoreBankingConnector:
            def __init__(self, config):
                self.api_endpoint = config['api_endpoint']
                self.auth_token = config['auth_token']
                
            def sync_account_data(self, asset_ids):
                """Synchronize account information with blockchain records"""
                
                for asset_id in asset_ids:
                    # Fetch blockchain provenance
                    blockchain_record = analyzer.fetch_transaction_data(asset_id)
                    
                    # Get corresponding bank account
                    bank_account = self.get_bank_account(asset_id)
                    
                    # Compare balances and ownership
                    discrepancies = self.compare_records(blockchain_record, bank_account)
                    
                    if discrepancies:
                        self.flag_discrepancy(asset_id, discrepancies)
                        
            def get_bank_account(self, asset_id):
                """Retrieve bank account data"""
                response = requests.get(
                    f"{self.api_endpoint}/accounts/{asset_id}",
                    headers={'Authorization': f'Bearer {self.auth_token}'}
                )
                return response.json()
                
            def compare_records(self, blockchain_record, bank_record):
                """Identify discrepancies between systems"""
                discrepancies = []
                
                # Compare current owner
                blockchain_owner = blockchain_record['current_owner']
                bank_owner = bank_record['account_holder']
                
                if blockchain_owner != bank_owner:
                    discrepancies.append({
                        'type': 'ownership_mismatch',
                        'blockchain_owner': blockchain_owner,
                        'bank_owner': bank_owner
                    })
                    
                # Compare balances
                blockchain_value = blockchain_record['current_value']
                bank_balance = bank_record['balance']
                
                if abs(blockchain_value - bank_balance) > 0.01:  # $0.01 tolerance
                    discrepancies.append({
                        'type': 'balance_mismatch',
                        'blockchain_value': blockchain_value,
                        'bank_balance': bank_balance,
                        'difference': abs(blockchain_value - bank_balance)
                    })
                    
                return discrepancies
                
        return CoreBankingConnector(bank_system_config)
        
    def connect_risk_system(self, risk_config):
        """Integrate with enterprise risk management"""
        
        class RiskIntegration:
            def __init__(self, config):
                self.risk_api = config['api_endpoint']
                self.risk_thresholds = config['thresholds']
                
            def calculate_provenance_risk_score(self, asset_id):
                """Calculate risk score based on provenance data"""
                
                # Get complete transaction history
                history = analyzer.fetch_transaction_data(asset_id)
                
                risk_factors = {
                    'ownership_stability': self.assess_ownership_stability(history),
                    'transaction_frequency': self.assess_transaction_frequency(history),
                    'value_volatility': self.assess_value_volatility(history),
                    'counterparty_risk': self.assess_counterparty_risk(history)
                }
                
                # Calculate weighted risk score
                weights = {'ownership_stability': 0.3, 'transaction_frequency': 0.2, 
                          'value_volatility': 0.3, 'counterparty_risk': 0.2}
                
                risk_score = sum(risk_factors[factor] * weights[factor] 
                               for factor in risk_factors)
                
                # Update enterprise risk system
                self.update_risk_system(asset_id, risk_score, risk_factors)
                
                return risk_score
                
            def assess_ownership_stability(self, history):
                """Lower score = more stable ownership"""
                transactions = history['transactions']
                
                if len(transactions) <= 1:
                    return 0.1  # Very stable
                    
                # Calculate ownership change frequency
                ownership_changes = len(set(tx['current_owner'] for tx in transactions))
                time_span = transactions[-1]['timestamp'] - transactions[0]['timestamp']
                
                # Changes per year
                annual_change_rate = ownership_changes / (time_span / (365 * 24 * 3600))
                
                # Normalize to 0-1 scale
                return min(annual_change_rate / 10, 1.0)
                
            def update_risk_system(self, asset_id, risk_score, risk_factors):
                """Update enterprise risk management system"""
                
                risk_update = {
                    'asset_id': asset_id,
                    'provenance_risk_score': risk_score,
                    'risk_factors': risk_factors,
                    'last_updated': datetime.now().isoformat(),
                    'next_review_date': (datetime.now() + timedelta(days=30)).isoformat()
                }
                
                response = requests.post(
                    f"{self.risk_api}/provenance-risk-updates",
                    json=risk_update
                )
                
                if response.status_code != 200:
                    print(f"Failed to update risk system: {response.text}")
                    
        return RiskIntegration(risk_config)

# Configure enterprise integrations
enterprise_config = {
    'core_banking': {
        'api_endpoint': 'https://bank-core-api.internal',
        'auth_token': 'your-bank-api-token'
    },
    'risk_management': {
        'api_endpoint': 'https://risk-mgmt-api.internal',
        'thresholds': {
            'high_risk': 0.8,
            'medium_risk': 0.5,
            'low_risk': 0.2
        }
    }
}

integration = EnterpriseIntegration()
core_banking = integration.connect_core_banking(enterprise_config['core_banking'])
risk_system = integration.connect_risk_system(enterprise_config['risk_management'])