""" 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" )