Compliance & Risk Frameworks

Comprehensive Compliance Management System

import asyncio
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Callable, Any
from datetime import datetime, timedelta
from enum import Enum
import json
import hashlib
import pandas as pd
from cryptography.fernet import Fernet
import smtplib
from email.mime.text import MimeText
from email.mime.multipart import MimeMultipart
import os
import re

class ComplianceStatus(Enum):
    COMPLIANT = "compliant"
    NON_COMPLIANT = "non_compliant"
    PENDING_REVIEW = "pending_review"
    UNDER_INVESTIGATION = "under_investigation"
    REMEDIATED = "remediated"

class RiskLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

class RegulationType(Enum):
    AML = "anti_money_laundering"
    KYC = "know_your_customer"
    OFAC = "ofac_sanctions"
    GDPR = "gdpr_data_protection"
    SEC = "sec_securities"
    CFTC = "cftc_commodities"
    BAIR = "bail_insurance_requirements"

@dataclass
class ComplianceRule:
    """Individual compliance rule"""
    rule_id: str
    regulation: RegulationType
    description: str
    severity: RiskLevel
    automation_level: str  # "fully_automated", "semi_automated", "manual"
    check_frequency: str   # "real_time", "daily", "weekly", "monthly"
    remediation_required: bool = False
    requires_human_review: bool = False

@dataclass
class ComplianceViolation:
    """Represents a compliance violation"""
    violation_id: str
    rule_id: str
    timestamp: datetime
    entity_id: str
    severity: RiskLevel
    description: str
    status: ComplianceStatus
    assigned_to: Optional[str] = None
    due_date: Optional[datetime] = None
    resolution_notes: str = ""
    automated_detection: bool = False
    
    @property
    def days_overdue(self) -> int:
        if self.due_date and self.status != ComplianceStatus.REMEDIATED:
            return (datetime.now() - self.due_date).days
        return 0

@dataclass
class RiskAssessment:
    """Enterprise risk assessment"""
    assessment_id: str
    timestamp: datetime
    entity_id: str
    assessment_type: str
    risk_score: float
    risk_level: RiskLevel
    key_risks: List[str]
    mitigation_measures: List[str]
    next_review_date: datetime
    assessor: str
    approved_by: Optional[str] = None

class ComplianceFramework:
    """Comprehensive compliance management system"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.logger = logging.getLogger(__name__)
        
        # Database connections
        self.compliance_db = None
        self.transaction_db = None
        self.customer_db = None
        
        # Rule engine
        self.compliance_rules = {}
        self.rule_executors = {}
        
        # Monitoring and alerting
        self.alert_thresholds = {}
        self.notification_channels = []
        
        # Encryption for sensitive data
        self.cipher = Fernet(config.get('encryption_key', Fernet.generate_key()))
        
        # Reporting
        self.report_templates = {}
        self.scheduled_reports = []
        
        # Audit trail
        self.audit_log = []
        
    async def initialize(self):
        """Initialize compliance framework"""
        self.logger.info("Initializing compliance framework...")
        
        # Load compliance rules
        await self._load_compliance_rules()
        
        # Initialize monitoring systems
        await self._initialize_monitoring()
        
        # Set up alert systems
        await self._setup_alerting()
        
        # Start background processes
        asyncio.create_task(self._compliance_monitoring_loop())
        asyncio.create_task(self._risk_assessment_loop())
        asyncio.create_task(self._regulatory_reporting_loop())
        
        self.logger.info("Compliance framework initialized")
    
    async def _load_compliance_rules(self):
        """Load all compliance rules from database"""
        # Sample compliance rules for MEV operations
        rules = [
            ComplianceRule(
                rule_id="AML_001",
                regulation=RegulationType.AML,
                description="Monitor transactions above $10,000 for suspicious patterns",
                severity=RiskLevel.HIGH,
                automation_level="fully_automated",
                check_frequency="real_time",
                requires_human_review=True
            ),
            ComplianceRule(
                rule_id="KYC_002",
                regulation=RegulationType.KYC,
                description="Verify customer identity for accounts above $50,000",
                severity=RiskLevel.HIGH,
                automation_level="semi_automated",
                check_frequency="real_time",
                remediation_required=True
            ),
            ComplianceRule(
                rule_id="OFAC_003",
                regulation=RegulationType.OFAC,
                description="Screen all addresses against OFAC sanctions list",
                severity=RiskLevel.CRITICAL,
                automation_level="fully_automated",
                check_frequency="real_time"
            ),
            ComplianceRule(
                rule_id="MEV_004",
                regulation=RegulationType.SEC,
                description="Ensure MEV strategies comply with securities regulations",
                severity=RiskLevel.MEDIUM,
                automation_level="manual",
                check_frequency="daily",
                requires_human_review=True
            ),
            ComplianceRule(
                rule_id="DATA_005",
                regulation=RegulationType.GDPR,
                description="Ensure customer data protection and privacy compliance",
                severity=RiskLevel.HIGH,
                automation_level="semi_automated",
                check_frequency="weekly",
                remediation_required=True
            )
        ]
        
        # Store rules
        for rule in rules:
            self.compliance_rules[rule.rule_id] = rule
        
        self.logger.info(f"Loaded {len(rules)} compliance rules")
    
    async def check_transaction_compliance(self, transaction: Dict) -> List[ComplianceViolation]:
        """Check transaction against all applicable compliance rules"""
        violations = []
        
        for rule_id, rule in self.compliance_rules.items():
            try:
                # Execute rule check
                violation = await self._execute_compliance_rule(rule, transaction)
                if violation:
                    violations.append(violation)
                    
            except Exception as e:
                self.logger.error(f"Error executing rule {rule_id}: {e}")
        
        return violations
    
    async def _execute_compliance_rule(self, rule: ComplianceRule, transaction: Dict) -> Optional[ComplianceViolation]:
        """Execute individual compliance rule"""
        # Get rule-specific executor
        executor = self.rule_executors.get(rule.rule_id)
        
        if executor:
            return await executor(rule, transaction)
        
        # Default rule execution logic
        if rule.regulation == RegulationType.AML:
            return await self._check_aml_rule(rule, transaction)
        elif rule.regulation == RegulationType.KYC:
            return await self._check_kyc_rule(rule, transaction)
        elif rule.regulation == RegulationType.OFAC:
            return await self._check_ofac_rule(rule, transaction)
        elif rule.regulation == RegulationType.SEC:
            return await self._check_sec_rule(rule, transaction)
        
        return None
    
    async def _check_aml_rule(self, rule: ComplianceRule, transaction: Dict) -> Optional[ComplianceViolation]:
        """Check Anti-Money Laundering compliance"""
        amount_usd = transaction.get('amount_usd', 0)
        
        # Large transaction threshold
        if amount_usd >= 10000:
            # Check for suspicious patterns
            patterns = await self._analyze_transaction_patterns(transaction)
            
            if patterns['suspicious_score'] > 0.7:
                return ComplianceViolation(
                    violation_id=self._generate_violation_id(),
                    rule_id=rule.rule_id,
                    timestamp=datetime.now(),
                    entity_id=transaction.get('entity_id'),
                    severity=RiskLevel.HIGH,
                    description=f"Suspicious transaction pattern detected: {patterns['reasons']}",
                    status=ComplianceStatus.PENDING_REVIEW,
                    automated_detection=True
                )
        
        return None
    
    async def _check_kyc_rule(self, rule: ComplianceRule, transaction: Dict) -> Optional[ComplianceViolation]:
        """Check Know Your Customer compliance"""
        entity_id = transaction.get('entity_id')
        
        # Get customer KYC status
        kyc_status = await self._get_customer_kyc_status(entity_id)
        
        if kyc_status['status'] != 'verified':
            return ComplianceViolation(
                violation_id=self._generate_violation_id(),
                rule_id=rule.rule_id,
                timestamp=datetime.now(),
                entity_id=entity_id,
                severity=RiskLevel.HIGH,
                description=f"Unverified customer: {kyc_status['status']}",
                status=ComplianceStatus.PENDING_REVIEW,
                automated_detection=True
            )
        
        return None
    
    async def _check_ofac_rule(self, rule: ComplianceRule, transaction: Dict) -> Optional[ComplianceViolation]:
        """Check OFAC sanctions compliance"""
        addresses = [transaction.get('from_address'), transaction.get('to_address')]
        
        for address in addresses:
            if address and await self._check_sanctions_list(address):
                return ComplianceViolation(
                    violation_id=self._generate_violation_id(),
                    rule_id=rule.rule_id,
                    timestamp=datetime.now(),
                    entity_id=transaction.get('entity_id'),
                    severity=RiskLevel.CRITICAL,
                    description=f"Sanctioned address detected: {address}",
                    status=ComplianceStatus.UNDER_INVESTIGATION,
                    automated_detection=True
                )
        
        return None
    
    async def _check_sec_rule(self, rule: ComplianceRule, transaction: Dict) -> Optional[ComplianceViolation]:
        """Check SEC securities compliance for MEV operations"""
        # This would check if MEV strategy constitutes securities trading
        strategy_type = transaction.get('strategy_type')
        
        if strategy_type in ['front_running', 'insider_mev']:
            return ComplianceViolation(
                violation_id=self._generate_violation_id(),
                rule_id=rule.rule_id,
                timestamp=datetime.now(),
                entity_id=transaction.get('entity_id'),
                severity=RiskLevel.MEDIUM,
                description=f"Potentially non-compliant MEV strategy: {strategy_type}",
                status=ComplianceStatus.PENDING_REVIEW,
                requires_human_review=True
            )
        
        return None
    
    async def _analyze_transaction_patterns(self, transaction: Dict) -> Dict:
        """Analyze transaction for suspicious patterns"""
        # Sample pattern analysis logic
        patterns = {
            'structuring_detected': False,
            'velocity_anomaly': False,
            'geographic_risk': False,
            'counterparty_risk': False,
            'suspicious_score': 0.0,
            'reasons': []
        }
        
        amount = transaction.get('amount_usd', 0)
        
        # Structuring detection (amounts just below reporting thresholds)
        if 9000 <= amount <= 9999:
            patterns['structuring_detected'] = True
            patterns['suspicious_score'] += 0.4
            patterns['reasons'].append("Potential structuring detected")
        
        # Velocity anomaly
        entity_id = transaction.get('entity_id')
        recent_transactions = await self._get_recent_transactions(entity_id, hours=24)
        
        if len(recent_transactions) > 50:  # More than 50 transactions in 24 hours
            patterns['velocity_anomaly'] = True
            patterns['suspicious_score'] += 0.3
            patterns['reasons'].append("High transaction velocity")
        
        # Geographic risk
        if transaction.get('country') in ['High-Risk Jurisdiction']:
            patterns['geographic_risk'] = True
            patterns['suspicious_score'] += 0.2
            patterns['reasons'].append("High-risk jurisdiction")
        
        return patterns
    
    async def generate_compliance_report(self, start_date: datetime, end_date: datetime) -> Dict:
        """Generate comprehensive compliance report"""
        # Get violations in date range
        violations = await self._get_violations_in_range(start_date, end_date)
        
        # Calculate metrics
        total_violations = len(violations)
        high_severity = len([v for v in violations if v.severity == RiskLevel.HIGH])
        critical_severity = len([v for v in violations if v.severity == RiskLevel.CRITICAL])
        resolved_violations = len([v for v in violations if v.status == ComplianceStatus.REMEDIATED])
        
        # Rule compliance rates
        rule_compliance = {}
        for rule_id, rule in self.compliance_rules.items():
            rule_violations = [v for v in violations if v.rule_id == rule_id]
            total_checks = await self._get_rule_check_count(rule_id, start_date, end_date)
            
            compliance_rate = 1.0 - (len(rule_violations) / max(total_checks, 1))
            rule_compliance[rule_id] = {
                'compliance_rate': compliance_rate,
                'total_violations': len(rule_violations),
                'total_checks': total_checks
            }
        
        # Risk assessment
        risk_metrics = await self._calculate_risk_metrics(start_date, end_date)
        
        return {
            'report_period': {
                'start_date': start_date.isoformat(),
                'end_date': end_date.isoformat()
            },
            'executive_summary': {
                'total_violations': total_violations,
                'high_severity_violations': high_severity,
                'critical_violations': critical_severity,
                'resolution_rate': resolved_violations / max(total_violations, 1),
                'overall_compliance_score': 1.0 - (total_violations / max(sum(rule_compliance[r]['total_checks'] for r in rule_compliance), 1))
            },
            'violation_analysis': {
                'by_severity': {
                    'low': len([v for v in violations if v.severity == RiskLevel.LOW]),
                    'medium': len([v for v in violations if v.severity == RiskLevel.MEDIUM]),
                    'high': high_severity,
                    'critical': critical_severity
                },
                'by_status': {
                    'pending_review': len([v for v in violations if v.status == ComplianceStatus.PENDING_REVIEW]),
                    'under_investigation': len([v for v in violations if v.status == ComplianceStatus.UNDER_INVESTIGATION]),
                    'remediated': resolved_violations
                }
            },
            'rule_compliance': rule_compliance,
            'risk_metrics': risk_metrics,
            'recommendations': await self._generate_compliance_recommendations(violations),
            'regulatory_updates': await self._check_regulatory_updates()
        }
    
    async def create_incident_response_plan(self) -> Dict:
        """Create comprehensive incident response plan"""
        return {
            'incident_types': {
                'security_breach': {
                    'severity_levels': ['low', 'medium', 'high', 'critical'],
                    'response_time': {
                        'low': '24 hours',
                        'medium': '4 hours',
                        'high': '1 hour',
                        'critical': '15 minutes'
                    },
                    'escalation_matrix': [
                        'Security Team',
                        'Compliance Officer',
                        'Risk Committee',
                        'Board of Directors'
                    ]
                },
                'regulatory_violation': {
                    'response_steps': [
                        'Immediate containment',
                        'Legal review',
                        'Regulatory notification',
                        'Remediation planning',
                        'Communication strategy'
                    ],
                    'required_reporting': [
                        'SEC',
                        'CFTC',
                        'FinCEN',
                        'Foreign regulators'
                    ]
                },
                'operational_failure': {
                    'response_time': '30 minutes',
                    'backup_procedures': [
                        'Manual operation mode',
                        'Alternative execution venues',
                        'Customer communication',
                        'Recovery procedures'
                    ]
                }
            },
            'communication_protocols': {
                'internal': [
                    'Management team',
                    'Risk committee',
                    'Legal department',
                    'IT security'
                ],
                'external': [
                    'Regulatory authorities',
                    'Customers',
                    'Vendors',
                    'Media'
                ]
            },
            'documentation_requirements': [
                'Incident timeline',
                'Root cause analysis',
                'Impact assessment',
                'Remediation actions',
                'Lessons learned'
            ]
        }
    
    async def implement_continuous_monitoring(self) -> Dict:
        """Implement continuous compliance monitoring"""
        monitoring_config = {
            'real_time_alerts': {
                'transaction_monitoring': {
                    'threshold_amount_usd': 10000,
                    'velocity_threshold': 50,  # transactions per hour
                    'velocity_window': '1h',
                    'sanctions_screening': True,
                    'pep_screening': True
                },
                'customer_monitoring': {
                    'kyc_renewal_alerts': True,
                    'risk_score_changes': True,
                    'account_activity_alerts': True,
                    'new_high_risk_customers': True
                },
                'system_monitoring': {
                    'compliance_system_health': True,
                    'data_quality_alerts': True,
                    'rule_performance_monitoring': True,
                    'false_positive_rates': True
                }
            },
            'automated_remediation': {
                'high_risk_transactions': 'auto_block',
                'sanctions_hits': 'immediate_escalation',
                'system_failures': 'auto_failover',
                'data_quality_issues': 'auto_correction'
            },
            'reporting_automation': {
                'daily_compliance_summary': True,
                'weekly_risk_report': True,
                'monthly_regulatory_report': True,
                'quarterly_board_report': True,
                'annual_comprehensive_audit': True
            },
            'quality_assurance': {
                'rule_validation': 'monthly',
                'model_validation': 'quarterly',
                'system_testing': 'weekly',
                'backup_system_testing': 'monthly'
            }
        }
        
        return monitoring_config
    
    # Helper methods
    async def _generate_violation_id(self) -> str:
        """Generate unique violation ID"""
        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
        return f"VIOL_{timestamp}_{hashlib.md5(str(datetime.now()).encode()).hexdigest()[:6]}"
    
    async def _get_customer_kyc_status(self, entity_id: str) -> Dict:
        """Get customer KYC status (placeholder implementation)"""
        # This would query actual customer database
        return {
            'status': 'verified',  # or 'pending', 'rejected', 'expired'
            'verification_date': datetime.now() - timedelta(days=30),
            'next_review_date': datetime.now() + timedelta(days=335)
        }
    
    async def _check_sanctions_list(self, address: str) -> bool:
        """Check address against sanctions list (placeholder implementation)"""
        # This would check against actual OFAC list
        return False  # Assume clean for demo
    
    async def _get_recent_transactions(self, entity_id: str, hours: int) -> List[Dict]:
        """Get recent transactions for entity (placeholder implementation)"""
        # This would query actual transaction database
        return []  # Return empty list for demo
    
    async def _get_violations_in_range(self, start_date: datetime, end_date: datetime) -> List[ComplianceViolation]:
        """Get violations in date range (placeholder implementation)"""
        # This would query actual compliance database
        return []
    
    async def _get_rule_check_count(self, rule_id: str, start_date: datetime, end_date: datetime) -> int:
        """Get number of times rule was checked (placeholder implementation)"""
        # This would query actual rule execution logs
        return 1000  # Placeholder
    
    async def _calculate_risk_metrics(self, start_date: datetime, end_date: datetime) -> Dict:
        """Calculate risk metrics for period (placeholder implementation)"""
        return {
            'average_risk_score': 2.3,
            'risk_score_trend': 'decreasing',
            'highest_risk_day': (datetime.now() - timedelta(days=5)).isoformat(),
            'risk_distribution': {
                'low': 60,
                'medium': 30,
                'high': 8,
                'critical': 2
            }
        }
    
    async def _generate_compliance_recommendations(self, violations: List[ComplianceViolation]) -> List[str]:
        """Generate compliance recommendations based on violations"""
        recommendations = []
        
        if len([v for v in violations if v.severity == RiskLevel.CRITICAL]) > 5:
            recommendations.append("Implement immediate review of all critical compliance processes")
        
        if len([v for v in violations if v.due_date and v.days_overdue > 30]) > 10:
            recommendations.append("Review and improve violation remediation processes")
        
        if any(v.requires_human_review for v in violations):
            recommendations.append("Increase staffing for manual compliance reviews")
        
        return recommendations
    
    async def _check_regulatory_updates(self) -> List[Dict]:
        """Check for recent regulatory updates"""
        return [
            {
                'regulation': 'SEC',
                'update_type': 'Guidance',
                'title': 'Digital Asset Securities Framework',
                'effective_date': '2024-03-01',
                'impact_assessment': 'medium'
            },
            {
                'regulation': 'CFTC',
                'update_type': 'Proposed Rule',
                'title': 'MEV Regulation for Commodity Markets',
                'comment_deadline': '2024-04-15',
                'impact_assessment': 'high'
            }
        ]
    
    async def _compliance_monitoring_loop(self):
        """Background loop for compliance monitoring"""
        while True:
            try:
                # Check for new transactions
                transactions = await self._get_pending_transactions()
                
                # Process each transaction
                for transaction in transactions:
                    violations = await self.check_transaction_compliance(transaction)
                    
                    # Handle violations
                    for violation in violations:
                        await self._handle_compliance_violation(violation)
                
                await asyncio.sleep(1)  # Check every second
                
            except Exception as e:
                self.logger.error(f"Error in compliance monitoring loop: {e}")
                await asyncio.sleep(10)
    
    async def _risk_assessment_loop(self):
        """Background loop for risk assessments"""
        while True:
            try:
                # Perform periodic risk assessments
                await self._perform_scheduled_risk_assessments()
                
                await asyncio.sleep(3600)  # Check every hour
                
            except Exception as e:
                self.logger.error(f"Error in risk assessment loop: {e}")
                await asyncio.sleep(60)
    
    async def _regulatory_reporting_loop(self):
        """Background loop for regulatory reporting"""
        while True:
            try:
                # Check if any reports are due
                await self._check_scheduled_reports()
                
                await asyncio.sleep(3600)  # Check every hour
                
            except Exception as e:
                self.logger.error(f"Error in regulatory reporting loop: {e}")
                await asyncio.sleep(60)
    
    # Placeholder methods for database operations
    async def _get_pending_transactions(self) -> List[Dict]:
        return []
    
    async def _handle_compliance_violation(self, violation: ComplianceViolation):
        self.logger.warning(f"Compliance violation detected: {violation.violation_id}")
    
    async def _perform_scheduled_risk_assessments(self):
        pass
    
    async def _check_scheduled_reports(self):
        pass

# Usage Example
async def main():
    # Initialize compliance framework
    config = {
        'encryption_key': Fernet.generate_key(),
        'database_url': 'postgresql://user:pass@localhost/compliance',
        'alert_email': 'compliance@company.com'
    }
    
    compliance_framework = ComplianceFramework(config)
    await compliance_framework.initialize()
    
    # Generate compliance report
    end_date = datetime.now()
    start_date = end_date - timedelta(days=30)
    
    report = await compliance_framework.generate_compliance_report(start_date, end_date)
    print(f"Compliance Score: {report['executive_summary']['overall_compliance_score']:.2%}")
    print(f"Total Violations: {report['executive_summary']['total_violations']}")
    
    # Create incident response plan
    incident_plan = await compliance_framework.create_incident_response_plan()
    
    # Implement continuous monitoring
    monitoring_config = await compliance_framework.implement_continuous_monitoring()
    
    print("Compliance framework operational")

if __name__ == "__main__":
    asyncio.run(main())