integrations/atom_finance_customization_service.py

"""
ATOM Finance Industry Customization Service
Regulatory compliant financial AI and risk management system
"""

import asyncio
import base64
from collections import Counter, defaultdict
from dataclasses import asdict, dataclass
from datetime import datetime, timedelta, timezone
from enum import Enum
import hashlib
import hmac
import json
import logging
import os
import time
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from urllib.parse import urlencode
import aiohttp
import httpx
import numpy as np
import pandas as pd
from pydantic import BaseModel, Field
from core.circuit_breaker import circuit_breaker
from core.rate_limiter import rate_limiter, should_retry, calculate_backoff
from core.audit_logger import log_integration_call, log_integration_error, log_integration_attempt, log_integration_complete
from fastapi import HTTPException


# Import existing ATOM services
try:
    from ai_enhanced_service import (
        AIModelType,
        AIRequest,
        AIResponse,
        AIServiceType,
        AITaskType,
        ai_enhanced_service,
    )
    from atom_ai_integration import atom_ai_integration
    from atom_discord_integration import atom_discord_integration
    from atom_enterprise_security_service import atom_enterprise_security_service
    from atom_google_chat_integration import atom_google_chat_integration
    from atom_hubspot_integration_service import atom_hubspot_integration_service
    from atom_quickbooks_integration_service import atom_quickbooks_integration_service
    from atom_slack_integration import atom_slack_integration
    from atom_teams_integration import atom_teams_integration
    from atom_telegram_integration import atom_telegram_integration
    from atom_video_ai_service import atom_video_ai_service
    from atom_voice_ai_service import atom_voice_ai_service
    from atom_voice_video_integration_service import atom_voice_video_integration_service
    from atom_whatsapp_integration import atom_whatsapp_integration
    from atom_workflow_automation_service import (
        AutomationPriority,
        AutomationStatus,
        atom_workflow_automation_service,
    )
    from atom_zendesk_integration_service import atom_zendesk_integration_service
    from atom_zoom_integration import atom_zoom_integration
except ImportError as e:
    logging.warning(f"Enterprise services not available: {e}")

# Configure logging
logger = logging.getLogger(__name__)

class FinanceComplianceStandard(Enum):
    """Finance compliance standards"""
    SOX = "sox"
    PCI_DSS = "pci_dss"
    GLBA = "glba"
    FFIEC = "ffiec"
    GDPR = "gdpr"
    CCPA = "ccpa"
    MiFID_II = "mifid_ii"
    KYC = "kyc"
    AML = "aml"
    BASEL_III = "basel_iii"

class TransactionType(Enum):
    """Financial transaction types"""
    DEPOSIT = "deposit"
    WITHDRAWAL = "withdrawal"
    TRANSFER = "transfer"
    PAYMENT = "payment"
    INVESTMENT = "investment"
    LOAN = "loan"
    TRADE = "trade"
    FOREIGN_EXCHANGE = "foreign_exchange"

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

class AccountType(Enum):
    """Account types"""
    CHECKING = "checking"
    SAVINGS = "savings"
    CREDIT_CARD = "credit_card"
    LOAN = "loan"
    INVESTMENT = "investment"
    BUSINESS = "business"
    TRUST = "trust"

class FinancialAnalyticsType(Enum):
    """Financial analytics types"""
    RISK_ASSESSMENT = "risk_assessment"
    FRAUD_DETECTION = "fraud_detection"
    PORTFOLIO_ANALYSIS = "portfolio_analysis"
    COMPLIANCE_MONITORING = "compliance_monitoring"
    CREDIT_SCORING = "credit_scoring"
    MARKET_ANALYSIS = "market_analysis"
    REVENUE_ANALYTICS = "revenue_analytics"
    PREDICTIVE_MODELING = "predictive_modeling"

@dataclass
class Customer:
    """Customer data model"""
    customer_id: str
    account_number: str
    first_name: str
    last_name: str
    date_of_birth: datetime
    ssn_hash: str
    email: str
    phone: str
    address: Dict[str, str]
    credit_score: float
    risk_level: RiskLevel
    account_type: AccountType
    account_balance: float
    credit_limit: float
    employment_status: str
    annual_income: float
    kyc_status: str
    kyc_documents: List[Dict[str, Any]]
    created_at: datetime
    last_updated: datetime
    metadata: Dict[str, Any]

@dataclass
class Transaction:
    """Transaction data model"""
    transaction_id: str
    customer_id: str
    account_number: str
    transaction_type: TransactionType
    amount: float
    currency: str
    timestamp: datetime
    merchant_category: str
    description: str
    card_number_hash: str
    ip_address: str
    device_fingerprint: str
    location: Dict[str, str]
    fraud_score: float
    compliance_flags: List[str]
    status: str
    created_at: datetime
    metadata: Dict[str, Any]

@dataclass
class LoanApplication:
    """Loan application data model"""
    application_id: str
    customer_id: str
    loan_type: str
    loan_amount: float
    loan_term: int
    interest_rate: float
    purpose: str
    collateral: Dict[str, Any]
    credit_check_result: Dict[str, Any]
    risk_assessment: Dict[str, Any]
    approval_status: str
    approval_date: Optional[datetime]
    funded_date: Optional[datetime]
    created_at: datetime
    metadata: Dict[str, Any]

@dataclass
class FinancialAnalytics:
    """Financial analytics data model"""
    analytics_id: str
    analytics_type: FinancialAnalyticsType
    time_period: str
    start_date: datetime
    end_date: datetime
    department: str
    metrics: Dict[str, Any]
    insights: List[str]
    recommendations: List[str]
    created_at: datetime
    metadata: Dict[str, Any]

class AtomFinanceCustomizationService:
    """Advanced Finance Industry Customization Service"""
    
    def __init__(self, tenant_id: str = "default", config: Dict[str, Any] = None):
        if config is None:
            config = {}
        self.config = config
        self.db = config.get('database')
        self.cache = config.get('cache')
        
        # Finance API configuration
        self.finance_config = {
            'sox_compliance': config.get('sox_compliance', True),
            'pci_dss_compliance': config.get('pci_dss_compliance', True),
            'glba_compliance': config.get('glba_compliance', True),
            'ffiec_compliance': config.get('ffiec_compliance', True),
            'gdpr_compliance': config.get('gdpr_compliance', True),
            'kyc_required': config.get('kyc_required', True),
            'aml_monitoring': config.get('aml_monitoring', True),
            'fraud_detection': config.get('fraud_detection', True),
            'risk_assessment': config.get('risk_assessment', True),
            'credit_scoring': config.get('credit_scoring', True),
            'financial_ai_enabled': config.get('financial_ai_enabled', True),
            'predictive_modeling': config.get('predictive_modeling', True),
            'portfolio_management': config.get('portfolio_management', True),
            'compliance_monitoring': config.get('compliance_monitoring', True),
            'automated_reporting': config.get('automated_reporting', True),
            'real_time_monitoring': config.get('real_time_monitoring', True),
            'banking_core_integration': config.get('banking_core_integration', True),
            'trading_system_integration': config.get('trading_system_integration', True),
            'credit_bureau_integration': config.get('credit_bureau_integration', True),
            'regulatory_reporting': config.get('regulatory_reporting', True)
        }
        
        # API endpoints
        self.api_endpoints = {
            'customers': '/api/v1/customers',
            'accounts': '/api/v1/accounts',
            'transactions': '/api/v1/transactions',
            'loans': '/api/v1/loans',
            'credit_cards': '/api/v1/credit_cards',
            'investments': '/api/v1/investments',
            'risk_assessment': '/api/v1/risk_assessment',
            'fraud_detection': '/api/v1/fraud_detection',
            'compliance': '/api/v1/compliance',
            'analytics': '/api/v1/analytics',
            'reporting': '/api/v1/reporting'
        }
        
        # Integration state
        self.is_initialized = False
        self.compliance_standards: List[FinanceComplianceStandard] = []
        self.encryption_keys: Dict[str, str] = {}
        self.audit_logs: List[Dict[str, Any]] = []
        self.fraud_rules: Dict[str, Dict[str, Any]] = {}
        self.risk_models: Dict[str, Dict[str, Any]] = {}
        self.credit_scoring_models: Dict[str, Dict[str, Any]] = {}
        
        # Banking core integration
        self.banking_core_integration = None
        if self.finance_config['banking_core_integration']:
            self.banking_core_integration = self._initialize_banking_core_integration()
        
        # Trading system integration
        self.trading_system_integration = None
        if self.finance_config['trading_system_integration']:
            self.trading_system_integration = self._initialize_trading_system_integration()
        
        # Credit bureau integration
        self.credit_bureau_integration = None
        if self.finance_config['credit_bureau_integration']:
            self.credit_bureau_integration = self._initialize_credit_bureau_integration()
        
        # Enterprise integration (use safe defaults if optional services didn't import)
        self.enterprise_security = config.get('security_service') or globals().get('atom_enterprise_security_service')
        self.enterprise_automation = config.get('automation_service') or globals().get('atom_workflow_automation_service')
        self.ai_service = config.get('ai_service') or globals().get('ai_enhanced_service')

        # Platform integrations (use safe defaults if optional services didn't import)
        self.platform_integrations = {}
        _slack = globals().get('atom_slack_integration')
        if _slack:
            self.platform_integrations['slack'] = _slack
        _teams = globals().get('atom_teams_integration')
        if _teams:
            self.platform_integrations['teams'] = _teams
        _google_chat = globals().get('atom_google_chat_integration')
        if _google_chat:
            self.platform_integrations['google_chat'] = _google_chat
        _discord = globals().get('atom_discord_integration')
        if _discord:
            self.platform_integrations['discord'] = _discord
        _telegram = globals().get('atom_telegram_integration')
        if _telegram:
            self.platform_integrations['telegram'] = _telegram
        _whatsapp = globals().get('atom_whatsapp_integration')
        if _whatsapp:
            self.platform_integrations['whatsapp'] = _whatsapp
        _zoom = globals().get('atom_zoom_integration')
        if _zoom:
            self.platform_integrations['zoom'] = _zoom
        
        # Analytics and monitoring
        self.analytics_metrics = {
            'total_customers': 0,
            'active_accounts': 0,
            'total_transactions': 0,
            'transaction_volume_today': 0,
            'fraudulent_transactions': 0,
            'high_risk_transactions': 0,
            'compliance_violations': 0,
            'loan_applications': 0,
            'loan_approvals': 0,
            'credit_score_average': 0.0,
            'fraud_detection_rate': 0.0,
            'risk_assessment_accuracy': 0.0,
            'compliance_monitoring_efficiency': 0.0,
            'customer_satisfaction': 0.0,
            'revenue_growth': 0.0,
            'portfolio_performance': 0.0,
            'transaction_types': defaultdict(int),
            'risk_level_distribution': defaultdict(int),
            'compliance_standards_met': defaultdict(int)
        }
        
        # Performance metrics
        self.performance_metrics = {
            'api_response_time': 0.0,
            'fraud_detection_time': 0.0,
            'risk_assessment_time': 0.0,
            'compliance_check_time': 0.0,
            'credit_scoring_time': 0.0,
            'transaction_processing_time': 0.0,
            'financial_ai_processing_time': 0.0,
            'banking_core_sync_time': 0.0
        }
        
        logger.info("Finance Customization Service initialized")
    
    async def initialize(self) -> bool:
        """Initialize Finance Customization Service"""
        try:
            # Setup finance compliance standards
            await self._setup_finance_compliance_standards()
            
            # Initialize banking core integration
            if self.banking_core_integration:
                await self._initialize_banking_core_connection()
            
            # Initialize trading system integration
            if self.trading_system_integration:
                await self._initialize_trading_system_connection()
            
            # Initialize credit bureau integration
            if self.credit_bureau_integration:
                await self._initialize_credit_bureau_connection()
            
            # Setup encryption and security
            await self._setup_encryption_and_security()
            
            # Setup fraud detection
            if self.finance_config['fraud_detection']:
                await self._setup_fraud_detection()
            
            # Setup risk assessment
            if self.finance_config['risk_assessment']:
                await self._setup_risk_assessment()
            
            # Setup credit scoring
            if self.finance_config['credit_scoring']:
                await self._setup_credit_scoring()
            
            # Setup compliance monitoring
            if self.finance_config['compliance_monitoring']:
                await self._setup_compliance_monitoring()
            
            # Setup financial AI features
            if self.finance_config['financial_ai_enabled']:
                await self._setup_financial_ai()
            
            # Setup integrations
            await self._setup_integrations()
            
            # Load existing data
            await self._load_existing_data()
            
            # Start real-time monitoring
            if self.finance_config['real_time_monitoring']:
                await self._start_real_time_monitoring()
            
            self.is_initialized = True
            logger.info("Finance Customization Service initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"Error initializing Finance Customization Service: {e}")
            return False
    
    async def create_customer(self, customer_data: Dict[str, Any], platform: str = None) -> Dict[str, Any]:
        """Create new customer with finance compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "initialize", locals())
        try:
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )

            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )

            start_time = time.time()
            # Update analytics
            self.analytics_metrics['total_customers'] += 1
            # Finance compliance check
            if self.finance_config['sox_compliance']:
                compliance_check = await self._perform_finance_compliance_check(customer_data)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            # KYC verification
            if self.finance_config['kyc_required']:
                kyc_verification = await self._perform_kyc_verification(customer_data)
                if not kyc_verification['passed']:
                    return {'success': False, 'error': kyc_verification['reason']}
            # Credit scoring
            if self.finance_config['credit_scoring']:
                credit_score = await self._calculate_credit_score(customer_data)
                customer_data['credit_score'] = credit_score
                # Determine risk level based on credit score
                if credit_score >= 750:
                    customer_data['risk_level'] = RiskLevel.LOW
                elif credit_score >= 650:
                    customer_data['risk_level'] = RiskLevel.MEDIUM
                elif credit_score >= 550:
                    customer_data['risk_level'] = RiskLevel.HIGH
                else:
                    customer_data['risk_level'] = RiskLevel.CRITICAL
            # Financial AI analysis
            if self.finance_config['financial_ai_enabled']:
                ai_analysis = await self._analyze_customer_with_financial_ai(customer_data)
                customer_data.update(ai_analysis)
            # Encrypt sensitive data
            encrypted_data = await self._encrypt_customer_data(customer_data)
            # Prepare customer payload
            customer_payload = {
                'customer_id': encrypted_data['customer_id'],
                'account_number': encrypted_data['account_number'],
                'first_name': encrypted_data['first_name'],
                'last_name': encrypted_data['last_name'],
                'date_of_birth': encrypted_data['date_of_birth'].isoformat(),
                'ssn_hash': encrypted_data['ssn_hash'],
                'email': encrypted_data['email'],
                'phone': encrypted_data['phone'],
                'address': encrypted_data['address'],
                'credit_score': encrypted_data['credit_score'],
                'risk_level': encrypted_data['risk_level'].value,
                'account_type': encrypted_data['account_type'].value,
                'account_balance': encrypted_data['account_balance'],
                'credit_limit': encrypted_data['credit_limit'],
                'employment_status': encrypted_data['employment_status'],
                'annual_income': encrypted_data['annual_income'],
                'kyc_status': encrypted_data['kyc_status'],
                'kyc_documents': encrypted_data['kyc_documents'],
                'created_at': datetime.utcnow().isoformat(),
                'last_updated': datetime.utcnow().isoformat(),
                'metadata': {
                    'created_by': 'atom_finance_service',
                    'sox_compliant': True,
                    'pci_dss_compliant': True,
                    'encryption_enabled': True
                }
            }
            # Create customer via API
            headers = await self._get_auth_headers()
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.config.get('base_url')}{self.api_endpoints['customers']}",
                    headers=headers,
                    json=customer_payload,
                    timeout=30.0
                )
                if response.status_code == 201:
                    customer = response.json()
                    # Update performance metrics
                    creation_time = time.time() - start_time
                    self.performance_metrics['api_response_time'] = creation_time
                    # Log audit trail
                    await self._log_audit_event('customer_created', customer_data, encrypted_data)
                    # Sync with banking core
                    if self.banking_core_integration:
                        await self._sync_customer_to_banking_core(customer)
                    # Notify relevant platforms
                    if platform and platform in self.platform_integrations:
                        await self._notify_platform_customer_created(customer, platform)
                    # Trigger workflows
                    await self._trigger_customer_workflows(customer, 'created')
                    logger.info(f"Customer created successfully: {customer['customer_id']}")
                    return {
                        'success': True,
                        'customer': customer,
                        'customer_id': customer['customer_id'],
                        'credit_score': customer_data['credit_score'],
                        'risk_level': customer_data['risk_level'].value,
                        'creation_time': creation_time
                    }
                else:
                    error_msg = f"Failed to create customer: {response.status_code} - {response.text}"
                    logger.error(error_msg)
                    return {'success': False, 'error': error_msg}
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            logger.error(f"Error creating customer: {e}")
            return {'success': False, 'error': str(e)}
    
    async def process_transaction(self, transaction_data: Dict[str, Any], platform: str = None) -> Dict[str, Any]:
        """Process transaction with fraud detection"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "create_customer", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )
        try:
            start_time = time.time()
            
            # Update analytics
            self.analytics_metrics['total_transactions'] += 1
            self.analytics_metrics['transaction_volume_today'] += transaction_data.get('amount', 0.0)
            self.analytics_metrics['transaction_types'][transaction_data.get('transaction_type', 'transfer').value] += 1
            
            # Finance compliance check
            if self.finance_config['pci_dss_compliance']:
                compliance_check = await self._perform_pci_dss_compliance_check(transaction_data)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            
            # Fraud detection
            if self.finance_config['fraud_detection']:
                fraud_score = await self._calculate_fraud_score(transaction_data)
                transaction_data['fraud_score'] = fraud_score
                
                # Determine if transaction is fraudulent
                if fraud_score > 0.7:
                    transaction_data['status'] = 'flagged_for_review'
                    self.analytics_metrics['fraudulent_transactions'] += 1
                elif fraud_score > 0.5:
                    transaction_data['status'] = 'high_risk'
                    self.analytics_metrics['high_risk_transactions'] += 1
                else:
                    transaction_data['status'] = 'approved'
            
            # Risk assessment
            if self.finance_config['risk_assessment']:
                risk_assessment = await self._perform_transaction_risk_assessment(transaction_data)
                transaction_data['risk_assessment'] = risk_assessment
            
            # Financial AI analysis
            if self.finance_config['financial_ai_enabled']:
                ai_analysis = await self._analyze_transaction_with_financial_ai(transaction_data)
                transaction_data.update(ai_analysis)
            
            # Encrypt sensitive data
            encrypted_data = await self._encrypt_transaction_data(transaction_data)
            
            # Prepare transaction payload
            transaction_payload = {
                'transaction_id': encrypted_data['transaction_id'],
                'customer_id': encrypted_data['customer_id'],
                'account_number': encrypted_data['account_number'],
                'transaction_type': encrypted_data['transaction_type'].value,
                'amount': encrypted_data['amount'],
                'currency': encrypted_data['currency'],
                'timestamp': encrypted_data['timestamp'].isoformat(),
                'merchant_category': encrypted_data['merchant_category'],
                'description': encrypted_data['description'],
                'card_number_hash': encrypted_data['card_number_hash'],
                'ip_address': encrypted_data['ip_address'],
                'device_fingerprint': encrypted_data['device_fingerprint'],
                'location': encrypted_data['location'],
                'fraud_score': encrypted_data['fraud_score'],
                'compliance_flags': encrypted_data['compliance_flags'],
                'status': encrypted_data['status'],
                'created_at': datetime.utcnow().isoformat(),
                'metadata': {
                    'processed_by': 'atom_finance_service',
                    'sox_compliant': True,
                    'pci_dss_compliant': True,
                    'fraud_detection_enabled': self.finance_config['fraud_detection']
                }
            }
            
            # Process transaction via API
            headers = await self._get_auth_headers()
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.config.get('base_url')}{self.api_endpoints['transactions']}",
                    headers=headers,
                    json=transaction_payload,
                    timeout=30.0
                )
                
                if response.status_code == 201:
                    transaction = response.json()
                    
                    # Update performance metrics
                    processing_time = time.time() - start_time
                    self.performance_metrics['transaction_processing_time'] = processing_time
                    
                    # Log audit trail
                    await self._log_audit_event('transaction_processed', transaction_data, encrypted_data)
                    
                    # Sync with banking core
                    if self.banking_core_integration:
                        await self._sync_transaction_to_banking_core(transaction)
                    
                    # Notify relevant platforms
                    if platform and platform in self.platform_integrations:
                        await self._notify_platform_transaction_processed(transaction, platform)
                    
                    # Trigger workflows
                    await self._trigger_transaction_workflows(transaction, 'processed')
                    
                    logger.info(f"Transaction processed successfully: {transaction['transaction_id']}")
                    return {
                        'success': True,
                        'transaction': transaction,
                        'transaction_id': transaction['transaction_id'],
                        'fraud_score': transaction_data['fraud_score'],
                        'status': transaction['status'],
                        'processing_time': processing_time
                    }
                else:
                    error_msg = f"Failed to process transaction: {response.status_code} - {response.text}"
                    logger.error(error_msg)
                    return {'success': False, 'error': error_msg}
                    
        except Exception as e:
            logger.error(f"Error processing transaction: {e}")
            return {'success': False, 'error': str(e)}
    
    async def generate_financial_analytics(self, analytics_type: FinancialAnalyticsType,
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "generate_financial_analytics", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )
                                        time_period: str = '7d', department: str = None) -> Dict[str, Any]:
        """Generate financial analytics with compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "process_transaction", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )
        try:
            start_time = time.time()
            
            # Calculate date range
            end_date = datetime.utcnow()
            start_date = end_date - timedelta(days=7)  # Default to 7 days
            
            # Finance compliance check for analytics
            if self.finance_config['sox_compliance']:
                compliance_check = await self._verify_analytics_compliance(analytics_type)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            
            # Generate analytics based on type
            if analytics_type == FinancialAnalyticsType.RISK_ASSESSMENT:
                analytics_data = await self._generate_risk_assessment_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.FRAUD_DETECTION:
                analytics_data = await self._generate_fraud_detection_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.PORTFOLIO_ANALYSIS:
                analytics_data = await self._generate_portfolio_analysis_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.COMPLIANCE_MONITORING:
                analytics_data = await self._generate_compliance_monitoring_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.CREDIT_SCORING:
                analytics_data = await self._generate_credit_scoring_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.MARKET_ANALYSIS:
                analytics_data = await self._generate_market_analysis_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.REVENUE_ANALYTICS:
                analytics_data = await self._generate_revenue_analytics(start_date, end_date, department)
            elif analytics_type == FinancialAnalyticsType.PREDICTIVE_MODELING:
                analytics_data = await self._generate_predictive_modeling_analytics(start_date, end_date, department)
            else:
                analytics_data = {'error': 'Unsupported analytics type'}
            
            # Add financial AI-powered insights
            if self.finance_config['financial_ai_enabled']:
                insights = await self._generate_financial_ai_insights(analytics_data, analytics_type)
                analytics_data['ai_insights'] = insights
            
            # Create analytics object
            analytics = FinancialAnalytics(
                analytics_id=f"analytics_{int(time.time())}",
                analytics_type=analytics_type,
                time_period=time_period,
                start_date=start_date,
                end_date=end_date,
                department=department or 'all',
                metrics=analytics_data,
                insights=analytics_data.get('insights', []),
                recommendations=analytics_data.get('recommendations', []),
                created_at=datetime.utcnow(),
                metadata={'generated_by': 'atom_finance_service', 'sox_compliant': True}
            )
            
            # Update performance metrics
            generation_time = time.time() - start_time
            self.performance_metrics['api_response_time'] = generation_time
            
            return {
                'success': True,
                'analytics': asdict(analytics),
                'generation_time': generation_time
            }
            
        except Exception as e:
            logger.error(f"Error generating financial analytics: {e}")
            return {'success': False, 'error': str(e)}
    
    async def _calculate_credit_score(self, customer_data: Dict[str, Any]) -> float:
        """Calculate credit score using AI models"""
            start_time = time.time()
            # Prepare AI request for credit scoring
            ai_request = AIRequest(
                request_id=f"credit_scoring_{int(time.time())}",
                task_type=AITaskType.PREDICTION,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'customer_data': customer_data,
                    'context': 'credit_scoring',
                    'scoring_factors': [
                        'payment_history', 'credit_utilization', 'length_of_credit_history',
                        'new_credit_accounts', 'credit_mix', 'income_stability',
                        'employment_history', 'debt_to_income_ratio'
                    ]
                },
                context={
                    'platform': 'finance',
                    'task': 'credit_scoring',
                    'sox_compliant': True
                },
                platform='finance'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                credit_score = ai_response.output_data.get('credit_score', 650)
                scoring_factors = ai_response.output_data.get('scoring_factors', {})
            else:
                # Fallback to rule-based scoring
                credit_score = await self._rule_based_credit_scoring(customer_data)
                scoring_factors = {'method': 'rule_based'}
            # Update performance metrics
            scoring_time = time.time() - start_time
            self.performance_metrics['credit_scoring_time'] = scoring_time
            # Update analytics
            self.analytics_metrics['credit_score_average'] = (
                (self.analytics_metrics['credit_score_average'] * (self.analytics_metrics['total_customers'] - 1) + credit_score) / 
                self.analytics_metrics['total_customers']
            )
            return min(max(credit_score, 300), 850)  # Ensure score is between 300-850
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error calculating credit score: {e}")
            return 650  # Default score
    
    async def _calculate_fraud_score(self, transaction_data: Dict[str, Any]) -> float:
        """Calculate fraud score using ML models"""
            start_time = time.time()
            # Prepare AI request for fraud detection
            ai_request = AIRequest(
                request_id=f"fraud_detection_{int(time.time())}",
                task_type=AITaskType.PREDICTION,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'transaction_data': transaction_data,
                    'context': 'fraud_detection',
                    'risk_factors': [
                        'amount_anomaly', 'location_anomaly', 'time_anomaly',
                        'device_anomaly', 'merchant_anomaly', 'frequency_anomaly'
                    ]
                },
                context={
                    'platform': 'finance',
                    'task': 'fraud_detection',
                    'sox_compliant': True
                },
                platform='finance'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                fraud_score = ai_response.output_data.get('fraud_score', 0.1)
                risk_factors = ai_response.output_data.get('risk_factors', {})
            else:
                # Fallback to rule-based fraud detection
                fraud_score = await self._rule_based_fraud_detection(transaction_data)
                risk_factors = {'method': 'rule_based'}
            # Update performance metrics
            detection_time = time.time() - start_time
            self.performance_metrics['fraud_detection_time'] = detection_time
            # Update analytics
            if fraud_score > 0.7:
                self.analytics_metrics['fraud_detection_rate'] = (
                    (self.analytics_metrics['fraud_detection_rate'] * (self.analytics_metrics['total_transactions'] - 1) + 100) / 
                    self.analytics_metrics['total_transactions']
                )
            return min(max(fraud_score, 0.0), 1.0)  # Ensure score is between 0-1
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error calculating fraud score: {e}")
            return 0.1  # Default score
    
    async def _analyze_customer_with_financial_ai(self, customer_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze customer with financial AI"""
            start_time = time.time()
            # Prepare AI request for customer analysis
            ai_request = AIRequest(
                request_id=f"customer_analysis_{int(time.time())}",
                task_type=AITaskType.CONTENT_ANALYSIS,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'customer_data': customer_data,
                    'context': 'financial_customer_analysis',
                    'analysis_types': [
                        'profitability_prediction', 'churn_risk', 'product_suitability',
                        'risk_tolerance', 'investment_appetite', 'fraud_risk'
                    ]
                },
                context={
                    'platform': 'finance',
                    'task': 'customer_analysis',
                    'sox_compliant': True
                },
                platform='finance'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                analysis_result = ai_response.output_data
                financial_ai_suggestions = {
                    'profitability_score': analysis_result.get('profitability_score', 0.5),
                    'churn_risk_score': analysis_result.get('churn_risk_score', 0.2),
                    'recommended_products': analysis_result.get('recommended_products', []),
                    'risk_tolerance_level': analysis_result.get('risk_tolerance_level', 'moderate'),
                    'investment_appetite_score': analysis_result.get('investment_appetite_score', 0.5),
                    'customer_fraud_risk': analysis_result.get('customer_fraud_risk', 0.1),
                    'upsell_opportunities': analysis_result.get('upsell_opportunities', []),
                    'lifetime_value_prediction': analysis_result.get('lifetime_value_prediction', 10000.0)
                }
            else:
                financial_ai_suggestions = {
                    'profitability_score': 0.5,
                    'churn_risk_score': 0.2,
                    'recommended_products': [],
                    'risk_tolerance_level': 'moderate',
                    'investment_appetite_score': 0.5,
                    'customer_fraud_risk': 0.1,
                    'upsell_opportunities': [],
                    'lifetime_value_prediction': 10000.0
                }
            # Update performance metrics
            analysis_time = time.time() - start_time
            self.performance_metrics['financial_ai_processing_time'] = analysis_time
            return financial_ai_suggestions
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error analyzing customer with financial AI: {e}")
            return {
                'profitability_score': 0.5,
                'churn_risk_score': 0.2,
                'recommended_products': [],
                'risk_tolerance_level': 'moderate',
                'investment_appetite_score': 0.5,
                'customer_fraud_risk': 0.1,
                'upsell_opportunities': [],
                'lifetime_value_prediction': 10000.0
            }
    
    async def _setup_finance_compliance_standards(self):
        """Setup finance compliance standards"""
            # Initialize compliance standards
            self.compliance_standards = [
                FinanceComplianceStandard.SOX,
                FinanceComplianceStandard.PCI_DSS,
                FinanceComplianceStandard.GLBA,
                FinanceComplianceStandard.FFIEC,
                FinanceComplianceStandard.GDPR,
                FinanceComplianceStandard.KYC,
                FinanceComplianceStandard.AML
            ]
            # Setup encryption
            self.encryption_keys = {
                'data_encryption_key': os.getenv('FINANCE_ENCRYPTION_KEY', 'default_key'),
                'audit_encryption_key': os.getenv('FINANCE_AUDIT_KEY', 'default_audit_key')
            }
            logger.info("Finance compliance standards setup completed")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error setting up finance compliance standards: {e}")
            raise
    
    async def _rule_based_credit_scoring(self, customer_data: Dict[str, Any]) -> float:
        """Fallback rule-based credit scoring"""
            score = 850  # Start with perfect score
            # Income factor (max -100 points)
            income = customer_data.get('annual_income', 0)
            if income < 30000:
                score -= 100
            elif income < 50000:
                score -= 75
            elif income < 75000:
                score -= 50
            elif income < 100000:
                score -= 25
            # Employment status (max -50 points)
            employment = customer_data.get('employment_status', '').lower()
            if employment == 'unemployed':
                score -= 50
            elif employment == 'part_time':
                score -= 30
            elif employment == 'self_employed':
                score -= 20
            # Age factor (max -30 points)
            if 'date_of_birth' in customer_data:
                age = (datetime.utcnow() - customer_data['date_of_birth']).days // 365
                if age < 25:
                    score -= 30
                elif age < 35:
                    score -= 20
                elif age < 45:
                    score -= 10
            return min(max(score, 300), 850)  # Ensure score is between 300-850
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error in rule-based credit scoring: {e}")
            return 650
    
    async def _rule_based_fraud_detection(self, transaction_data: Dict[str, Any]) -> float:
        """Fallback rule-based fraud detection"""
            fraud_score = 0.0
            # Amount anomaly (max 0.3 points)
            amount = transaction_data.get('amount', 0)
            if amount > 10000:
                fraud_score += 0.3
            elif amount > 5000:
                fraud_score += 0.2
            elif amount > 1000:
                fraud_score += 0.1
            # Time anomaly (max 0.2 points)
            timestamp = transaction_data.get('timestamp', datetime.utcnow())
            if timestamp.hour < 6 or timestamp.hour > 22:
                fraud_score += 0.2
            # Location anomaly (max 0.2 points)
            location = transaction_data.get('location', {})
            if not location:
                fraud_score += 0.2
            # Frequency anomaly (max 0.3 points)
            # In production, this would check against recent transactions
            if transaction_data.get('high_frequency', False):
                fraud_score += 0.3
            return min(max(fraud_score, 0.0), 1.0)  # Ensure score is between 0-1
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error in rule-based fraud detection: {e}")
            return 0.1
    
    async def _encrypt_customer_data(self, customer_data: Dict[str, Any]) -> Dict[str, Any]:
        """Encrypt customer sensitive data"""
            start_time = time.time()
            # In production, this would use proper encryption algorithms
            encrypted_data = customer_data.copy()
            # Encrypt sensitive fields
            sensitive_fields = ['ssn_hash', 'first_name', 'last_name', 'address']
            for field in sensitive_fields:
                if field in encrypted_data:
                    # Simple encoding for demonstration - use proper encryption in production
                    encrypted_data[field] = base64.b64encode(str(encrypted_data[field]).encode()).decode()
            return encrypted_data
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error encrypting customer data: {e}")
            return customer_data
    
    async def _encrypt_transaction_data(self, transaction_data: Dict[str, Any]) -> Dict[str, Any]:
        """Encrypt transaction sensitive data"""
            start_time = time.time()
            # In production, this would use proper encryption algorithms
            encrypted_data = transaction_data.copy()
            # Encrypt sensitive fields
            sensitive_fields = ['card_number_hash', 'ip_address', 'device_fingerprint']
            for field in sensitive_fields:
                if field in encrypted_data:
                    # Simple encoding for demonstration - use proper encryption in production
                    encrypted_data[field] = base64.b64encode(str(encrypted_data[field]).encode()).decode()
            return encrypted_data
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error encrypting transaction data: {e}")
            return transaction_data
    
    async def _log_audit_event(self, event_type: str, original_data: Dict[str, Any], 
                              processed_data: Dict[str, Any]):
        """Log audit event for finance compliance"""
            start_time = time.time()
            audit_event = {
                'event_id': f"audit_{int(time.time())}",
                'event_type': event_type,
                'timestamp': datetime.utcnow().isoformat(),
                'user_id': 'atom_finance_service',
                'action': 'create',
                'resource_type': event_type.replace('_created', '').replace('_processed', ''),
                'original_data_hash': hashlib.sha256(str(original_data).encode()).hexdigest(),
                'processed_data_hash': hashlib.sha256(str(processed_data).encode()).hexdigest(),
                'compliance_standards': [standard.value for standard in self.compliance_standards],
                'encryption_used': True,
                'access_level': 'authorized'
            }
            self.audit_logs.append(audit_event)
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error logging audit event: {e}")
    
    async def _perform_finance_compliance_check(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Perform finance compliance check"""
            start_time = time.time()
            # Check for required data elements
            required_elements = ['first_name', 'last_name', 'date_of_birth', 'email']
            data_present = any(element in data for element in required_elements)
            # Check for proper encryption requirements
            encryption_required = self.finance_config['encryption_at_rest']
            # Check for audit logging requirements
            audit_required = self.finance_config['audit_logging']
            # Check for access control requirements
            access_control_required = self.finance_config['access_control']
            compliance_result = {
                'passed': True,
                'reason': 'Compliant with finance standards',
                'data_present': data_present,
                'encryption_required': encryption_required,
                'audit_required': audit_required,
                'access_control_required': access_control_required
            }
            # Update performance metrics
            compliance_time = time.time() - start_time
            self.performance_metrics['compliance_check_time'] = compliance_time
            return compliance_result
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error performing finance compliance check: {e}")
            return {'passed': False, 'reason': str(e)}
    
    async def _perform_kyc_verification(self, customer_data: Dict[str, Any]) -> Dict[str, Any]:
        """Perform KYC verification"""
            start_time = time.time()
            # Simple KYC verification - in production, this would integrate with KYC services
            kyc_documents = customer_data.get('kyc_documents', [])
            kyc_status = 'pending'
            # Check if required documents are present
            required_documents = ['id_proof', 'address_proof', 'income_proof']
            documents_present = any(doc.get('type') in required_documents for doc in kyc_documents)
            if documents_present:
                kyc_status = 'verified'
            else:
                kyc_status = 'pending_documents'
            kyc_result = {
                'passed': kyc_status == 'verified',
                'reason': f'KYC status: {kyc_status}',
                'kyc_status': kyc_status,
                'documents_present': documents_present,
                'required_documents': required_documents
            }
            # Update performance metrics
            kyc_time = time.time() - start_time
            self.performance_metrics['compliance_check_time'] = kyc_time
            return kyc_result
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error performing KYC verification: {e}")
            return {'passed': False, 'reason': str(e)}
    
    async def _get_auth_headers(self) -> Dict[str, str]:
        """Get authentication headers for finance API"""
        return {
            'Authorization': f"Bearer {self.config.get('finance_api_token')}",
            'Content-Type': 'application/json',
            'X-SOX-Compliant': 'true',
            'X-PCI-DSS-Compliant': 'true',
            'X-Encryption-Key': self.encryption_keys['data_encryption_key']
        }
    
    async def get_service_status(self) -> Dict[str, Any]:
        """Get Finance Customization service status"""
            return {
                'service': 'finance_customization',
                'status': 'active' if self.is_initialized else 'inactive',
                'finance_config': {
                    'sox_compliance': self.finance_config['sox_compliance'],
                    'pci_dss_compliance': self.finance_config['pci_dss_compliance'],
                    'glba_compliance': self.finance_config['glba_compliance'],
                    'ffiec_compliance': self.finance_config['ffiec_compliance'],
                    'gdpr_compliance': self.finance_config['gdpr_compliance'],
                    'kyc_required': self.finance_config['kyc_required'],
                    'aml_monitoring': self.finance_config['aml_monitoring'],
                    'fraud_detection': self.finance_config['fraud_detection'],
                    'risk_assessment': self.finance_config['risk_assessment'],
                    'credit_scoring': self.finance_config['credit_scoring'],
                    'financial_ai_enabled': self.finance_config['financial_ai_enabled'],
                    'predictive_modeling': self.finance_config['predictive_modeling'],
                    'portfolio_management': self.finance_config['portfolio_management'],
                    'compliance_monitoring': self.finance_config['compliance_monitoring'],
                    'automated_reporting': self.finance_config['automated_reporting'],
                    'real_time_monitoring': self.finance_config['real_time_monitoring'],
                    'banking_core_integration': self.finance_config['banking_core_integration'],
                    'trading_system_integration': self.finance_config['trading_system_integration'],
                    'credit_bureau_integration': self.finance_config['credit_bureau_integration'],
                    'regulatory_reporting': self.finance_config['regulatory_reporting']
                },
                'compliance_standards': [standard.value for standard in self.compliance_standards],
                'analytics_metrics': self.analytics_metrics,
                'performance_metrics': self.performance_metrics,
                'uptime': time.time() - (self._start_time if hasattr(self, '_start_time') else time.time())
            }
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            logger.error(f"Error getting service status: {e}")
            return {'error': str(e), 'service': 'finance_customization'}

    def _initialize_banking_core_integration(self):
        """Initialize banking core integration (stub)"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "get_service_status", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )
        logger.info("Banking core integration not implemented")
        return None

    def _initialize_trading_system_integration(self):
        """Initialize trading system integration (stub)"""
        logger.info("Trading system integration not implemented")
        return None

    def _initialize_credit_bureau_integration(self):
        """Initialize credit bureau integration (stub)"""
        logger.info("Credit bureau integration not implemented")
        return None

    async def close(self):
        """Close Finance Customization Service"""
            logger.info("Finance Customization Service closed")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error closing Finance Customization Service: {e}")

# Global Finance Customization service instance
_finance_config = {
    'sox_compliance': True,
    'pci_dss_compliance': True,
    'glba_compliance': True,
    'ffiec_compliance': True,
    'gdpr_compliance': True,
    'kyc_required': True,
    'aml_monitoring': True,
    'fraud_detection': True,
    'risk_assessment': True,
    'credit_scoring': True,
    'financial_ai_enabled': True,
    'predictive_modeling': True,
    'portfolio_management': True,
    'compliance_monitoring': True,
    'automated_reporting': True,
    'real_time_monitoring': True,
    'banking_core_integration': True,
    'trading_system_integration': True,
    'credit_bureau_integration': True,
    'regulatory_reporting': True,
    'base_url': os.getenv('FINANCE_API_URL', 'https://api.finance.example.com'),
    'finance_api_token': os.getenv('FINANCE_API_TOKEN', 'your-api-token'),
    'database': None,  # Would be actual database connection
    'cache': None,  # Would be actual cache client
}

# Add optional services if they were imported successfully
_security_service = globals().get('atom_enterprise_security_service')
if _security_service:
    _finance_config['security_service'] = _security_service
_automation_service = globals().get('atom_workflow_automation_service')
if _automation_service:
    _finance_config['automation_service'] = _automation_service
_ai_service = globals().get('ai_enhanced_service')
if _ai_service:
    _finance_config['ai_service'] = _ai_service

atom_finance_customization_service = AtomFinanceCustomizationService(_finance_config)
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_finance_customization", "close", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_finance_customization"):
                logger.warning(f"Circuit breaker is open for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_finance_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_finance_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_finance_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_finance_customization"
                )