AI-Powered Pig Butchering Scams: How Criminal Networks Use Technology to Scale Fraud and What Enterprises Can Do to Protect Their Users

# Simplified representation of identity generation workflow
class FakeIdentityGenerator:
    def __init__(self):
        self.face_generator = StyleGAN_Model()
        self.background_generator = AI_Background_Tool()
        self.profile_text_generator = GPT_Based_Model()
    
    def generate_persona(self, target_demographic):
        # Generate multiple consistent facial images
        base_face = self.face_generator.create_face(
            age_range=target_demographic.preferred_age,
            ethnicity=target_demographic.ethnicity,
            gender=target_demographic.gender_preference
        )
        
        # Create lifestyle photos with different contexts
        persona_images = []
        contexts = ['professional', 'casual', 'travel', 'luxury', 'social']
        
        for context in contexts:
            image = self.background_generator.composite_image(
                face=base_face,
                context=context,
                quality='high_resolution'
            )
            persona_images.append(image)
        
        # Generate consistent profile information
        profile_data = self.profile_text_generator.create_profile(
            persona_type='successful_investor',
            interests=target_demographic.interests,
            location=self.select_credible_location(),
            career=self.select_aspirational_career()
        )
        
        return Persona(images=persona_images, profile=profile_data)

class EnterpriseIdentityVerification:
    """
    Multi-layer identity verification system for detecting fake profiles
    """
    def verify_user_identity(self, profile_data, user_images):
        verification_checks = {
            'ai_generated_detection': self.detect_ai_generated_images(user_images),
            'behavioral_consistency': self.analyze_behavior_patterns(profile_data),
            'social_graph_analysis': self.verify_social_connections(profile_data),
            'device_fingerprinting': self.check_device_consistency(profile_data),
            'identity_documentation': self.verify_documents(profile_data),
            'biometric_liveness': self.perform_liveness_detection(profile_data)
        }
        
        risk_score = self.calculate_risk_score(verification_checks)
        
        return VerificationResult(
            approved=risk_score < RISK_THRESHOLD,
            risk_score=risk_score,
            checks=verification_checks
        )
    
    def detect_ai_generated_images(self, images):
        """
        Use AI detection models to identify synthetic images
        """
        detection_results = []
        
        for image in images:
            # Check for GAN artifacts
            gan_score = self.gan_artifact_detector.analyze(image)
            
            # Analyze facial inconsistencies
            facial_score = self.facial_consistency_analyzer.check(image)
            
            # Look for digital manipulation signs
            manipulation_score = self.manipulation_detector.scan(image)
            
            detection_results.append({
                'gan_artifacts': gan_score,
                'facial_consistency': facial_score,
                'manipulation_signs': manipulation_score
            })
        
        return self.aggregate_detection_scores(detection_results)

Criminal Compound Structure
├── Management Layer
│   ├── Operation Coordinators (planning, oversight)
│   ├── Technical Staff (platform maintenance, AI tools)
│   └── Training Personnel (operator education)
│
├── Operator Layer (100-500 people per compound)
│   ├── Initial Contact Operators (dating apps, social media)
│   ├── Relationship Building Operators (weeks 2-4)
│   ├── Investment Phase Operators (weeks 4+)
│   ├── Escalation Specialists (maximizing extraction)
│   └── Recovery Prevention (handling withdrawal requests)
│
└── Technology Infrastructure
    ├── AI Conversation Assistants
    ├── CRM Systems for Victim Tracking
    ├── Fake Trading Platforms
    ├── Communication Routing Systems
    └── Cryptocurrency Management Infrastructure

class AIConversationAssistant:
    """
    System used by scammers to manage multiple victim conversations
    """
    def __init__(self):
        self.language_model = GPT_Based_Conversation_Model()
        self.sentiment_analyzer = EmotionDetectionSystem()
        self.manipulation_optimizer = TacticSuggestionEngine()
        self.victim_profile_db = VictimCRMSystem()
    
    def generate_response(self, victim_id, incoming_message):
        # Retrieve victim profile and conversation history
        victim_profile = self.victim_profile_db.get_profile(victim_id)
        conversation_history = victim_profile.conversation_history
        
        # Analyze victim's emotional state
        emotional_state = self.sentiment_analyzer.analyze(incoming_message)
        
        # Determine optimal manipulation tactic
        current_phase = victim_profile.scam_phase
        tactic = self.manipulation_optimizer.suggest_tactic(
            emotional_state=emotional_state,
            scam_phase=current_phase,
            victim_vulnerability=victim_profile.vulnerability_profile
        )
        
        # Generate contextually appropriate response
        response_options = self.language_model.generate(
            conversation_history=conversation_history,
            incoming_message=incoming_message,
            persona=victim_profile.assigned_persona,
            tactic=tactic,
            num_options=3
        )
        
        # Present options to operator
        return ResponseSuggestions(
            recommended=response_options[0],
            alternatives=response_options[1:],
            tactic_explanation=tactic.reasoning
        )
    
    def track_victim_readiness(self, victim_id):
        """
        Assess when victim is ready for investment phase escalation
        """
        victim_profile = self.victim_profile_db.get_profile(victim_id)
        
        readiness_indicators = {
            'emotional_attachment': self.measure_attachment(victim_profile),
            'financial_disclosure': self.assess_financial_capacity(victim_profile),
            'trust_level': self.calculate_trust_score(victim_profile),
            'resistance_level': self.measure_skepticism(victim_profile),
            'time_invested': victim_profile.relationship_duration
        }
        
        readiness_score = self.calculate_readiness(readiness_indicators)
        
        if readiness_score > ESCALATION_THRESHOLD:
            return EscalationRecommendation(
                ready=True,
                suggested_initial_investment=self.calculate_safe_initial_amount(victim_profile),
                approach=self.suggest_introduction_method(victim_profile)
            )
        
        return EscalationRecommendation(
            ready=False,
            additional_grooming_needed=self.suggest_trust_building_activities(victim_profile)
        )

Fake Trading Platform Architecture
├── Frontend (User Interface)
│   ├── Web Application (React/Vue.js)
│   ├── Mobile Apps (iOS/Android native or React Native)
│   ├── Responsive Design
│   └── Professional Branding
│
├── Backend Systems
│   ├── User Management
│   ├── "Trading" Engine (fabricated)
│   ├── Database (user accounts, balances, transactions)
│   ├── Cryptocurrency Wallet Integration
│   └── Admin Control Panel
│
├── Data Integration
│   ├── Real-Time Market Data APIs (legitimate)
│   ├── Price Feed Integration (actual crypto/forex prices)
│   ├── News Feed Integration
│   └── Chart Libraries (TradingView, etc.)
│
├── Manipulation Systems
│   ├── Fabricated Profit Generation
│   ├── Withdrawal Control Logic
│   ├── "Fee" and "Tax" Triggers
│   └── Account Suspension Mechanisms
│
└── Payment Processing
    ├── Cryptocurrency Wallets (criminal-controlled)
    ├── Bank Accounts (money mules)
    ├── Payment Processors (sometimes complicit)
    └── Transaction Obfuscation

// Simplified fake trading platform frontend component
class TradingDashboard extends React.Component {
    constructor(props) {
        super(props);
        this.state = {
            balance: 0,
            positions: [],
            marketData: [],
            profitDisplay: 0
        };
    }
    
    async componentDidMount() {
        // Fetch real market data for authenticity
        const realMarketData = await this.fetchRealMarketData();
        
        // But show fabricated user balance and profits
        const fabricatedBalance = this.calculateFakeBalance();
        const fabricatedProfits = this.generateFakeProfits();
        
        this.setState({
            balance: fabricatedBalance,
            marketData: realMarketData,
            profitDisplay: fabricatedProfits
        });
        
        // Continuously update to appear live
        this.startRealTimeUpdates();
    }
    
    fetchRealMarketData() {
        // Use legitimate APIs (Binance, CoinGecko, etc.)
        return fetch('https://api.coingecko.com/api/v3/coins/markets')
            .then(response => response.json());
    }
    
    calculateFakeBalance() {
        // Show user their deposits plus fabricated profits
        const userDeposits = this.getUserTotalDeposits();
        const fakeProfit = userDeposits * 0.15; // Show 15% gains
        return userDeposits + fakeProfit;
    }
    
    generateFakeProfits() {
        // Create realistic profit fluctuations
        // Always net positive to encourage more deposits
        const baseProfit = this.state.profitDisplay || 1000;
        const fluctuation = (Math.random() - 0.3) * 100; // Biased positive
        return Math.max(0, baseProfit + fluctuation);
    }
    
    async handleWithdrawal(amount) {
        // This is where the fraud happens
        
        if (this.isFirstSmallWithdrawal(amount)) {
            // Allow small initial withdrawal to build trust
            return this.processLegitimateWithdrawal(amount);
        }
        
        // Block subsequent or large withdrawals with fabricated reasons
        return this.triggerWithdrawalBarrier(amount);
    }
    
    triggerWithdrawalBarrier(amount) {
        const barriers = [
            { type: 'tax', amount: amount * 0.20, reason: 'Capital gains tax required' },
            { type: 'verification', amount: amount * 0.10, reason: 'Account verification fee' },
            { type: 'insurance', amount: amount * 0.15, reason: 'Withdrawal insurance deposit' },
            { type: 'vip_upgrade', amount: 5000, reason: 'VIP status required for large withdrawals' }
        ];
        
        // Select barrier based on victim's remaining capacity
        return this.selectAndPresentBarrier(barriers);
    }
}

class FakeTradingPlatformBackend:
    """
    Backend system for fraudulent trading platform
    """
    def __init__(self):
        self.user_db = FraudDatabase()
        self.market_api = LegitimateMarketDataAPI()
        self.crypto_wallets = CriminalWallets()
        self.admin_controls = FraudControlPanel()
    
    def process_deposit(self, user_id, amount, crypto_txn_hash):
        """
        Process victim deposits (real cryptocurrency sent to criminal wallets)
        """
        # Verify legitimate deposit
        deposit_confirmed = self.crypto_wallets.verify_transaction(crypto_txn_hash)
        
        if deposit_confirmed:
            # Credit victim's fake account
            self.user_db.add_balance(user_id, amount)
            
            # Log for tracking
            self.admin_controls.log_successful_extraction(user_id, amount)
            
            # Immediately move funds to obfuscation wallets
            self.crypto_wallets.transfer_to_cold_storage(amount)
            
            return DepositResult(success=True, new_balance=self.get_fake_balance(user_id))
        
        return DepositResult(success=False, error="Transaction not found")
    
    def process_trade(self, user_id, trade_params):
        """
        Process 'trades' that are entirely fabricated
        """
        user_profile = self.user_db.get_user(user_id)
        
        # Generate fake trade execution
        fake_entry_price = self.market_api.get_current_price(trade_params.asset)
        
        # Determine trade outcome based on fraud strategy
        if self.should_show_profit(user_profile):
            # Show profits to encourage more deposits
            fake_exit_price = fake_entry_price * 1.05  # 5% profit
        else:
            # Show small loss occasionally for realism
            fake_exit_price = fake_entry_price * 0.98  # 2% loss
        
        # Update fake account balance
        profit_loss = self.calculate_fake_pnl(trade_params, fake_entry_price, fake_exit_price)
        self.user_db.update_balance(user_id, profit_loss)
        
        # Store fake trade record
        fake_trade = self.create_fake_trade_record(
            user_id, 
            trade_params, 
            fake_entry_price, 
            fake_exit_price
        )
        
        return fake_trade
    
    def should_show_profit(self, user_profile):
        """
        Determine whether to show profit or loss
        Strategy: Show mostly profits to encourage larger deposits
        """
        # Always show profit if user hasn't made maximum deposit
        if user_profile.total_deposits < user_profile.estimated_max_capacity:
            return True
        
        # Show mixed results once maximum extraction reached
        return random.random() > 0.3  # 70% profitable
    
    def process_withdrawal_request(self, user_id, amount):
        """
        Handle withdrawal requests (where fraud is revealed)
        """
        user_profile = self.user_db.get_user(user_id)
        
        # Strategy varies by extraction phase
        if self.is_trust_building_phase(user_profile):
            # Allow small withdrawal to build trust
            if amount < user_profile.total_deposits * 0.1:  # Less than 10%
                return self.process_small_trust_withdrawal(user_id, amount)
        
        # Otherwise, create barriers
        withdrawal_barrier = self.generate_withdrawal_barrier(user_profile, amount)
        
        return WithdrawalResult(
            approved=False,
            barrier=withdrawal_barrier,
            required_action=withdrawal_barrier.resolution_method
        )
    
    def generate_withdrawal_barrier(self, user_profile, amount):
        """
        Create fabricated reason why withdrawal cannot be processed
        """
        barriers = [
            {
                'type': 'tax_payment',
                'amount': amount * 0.20,
                'message': 'Tax payment required before withdrawal',
                'urgency': 'high'
            },
            {
                'type': 'verification_deposit',
                'amount': amount * 0.10,
                'message': 'Security verification deposit required',
                'urgency': 'medium'
            },
            {
                'type': 'account_upgrade',
                'amount': 5000,
                'message': 'Premium account required for large withdrawals',
                'urgency': 'medium'
            },
            {
                'type': 'market_volatility',
                'amount': 0,
                'message': 'Withdrawals temporarily suspended due to market conditions',
                'urgency': 'low',
                'delay_days': 7
            }
        ]
        
        # Select barrier based on victim's remaining resources
        optimal_barrier = self.select_optimal_barrier(barriers, user_profile)
        
        # Log for admin tracking
        self.admin_controls.log_withdrawal_block(user_id, amount, optimal_barrier)
        
        return optimal_barrier

class MarketDataIntegration:
    """
    Integration with real market data sources for authenticity
    """
    def __init__(self):
        self.binance_api = BinancePublicAPI()
        self.coinbase_api = CoinbasePublicAPI()
        self.forex_api = ForexDataAPI()
    
    def get_real_time_prices(self):
        """
        Fetch actual cryptocurrency and forex prices
        """
        return {
            'crypto': self.binance_api.get_ticker_prices(),
            'forex': self.forex_api.get_exchange_rates(),
            'timestamp': datetime.now()
        }
    
    def stream_price_updates(self, websocket_connection):
        """
        Stream real price updates to user's dashboard
        """
        # This creates the illusion of a real trading platform
        # while all user 'trading' is fabricated
        while websocket_connection.open:
            real_prices = self.get_real_time_prices()
            websocket_connection.send(real_prices)
            time.sleep(1)

class FraudAdminControlPanel:
    """
    Admin interface for managing fraud operations
    """
    def get_victim_dashboard(self):
        """
        Overview of all active victims
        """
        return {
            'total_victims': self.count_active_victims(),
            'total_deposits': self.sum_all_deposits(),
            'extraction_by_phase': self.group_by_phase(),
            'high_value_targets': self.identify_high_value_victims(),
            'at_risk_victims': self.identify_suspicious_victims(),
            'withdrawal_requests': self.pending_withdrawal_requests()
        }
    
    def manage_victim(self, victim_id):
        """
        Detailed victim management interface
        """
        victim = self.get_victim_profile(victim_id)
        
        return {
            'basic_info': victim.basic_info,
            'deposit_history': victim.deposits,
            'communication_log': victim.messages,
            'emotional_profile': victim.psychological_assessment,
            'estimated_capacity': victim.max_extractable_amount,
            'current_extraction': victim.total_deposits,
            'completion_percentage': (victim.total_deposits / victim.max_extractable_amount) * 100,
            'assigned_operator': victim.operator_id,
            'next_recommended_action': self.suggest_next_tactic(victim)
        }
    
    def control_withdrawal(self, withdrawal_request):
        """
        Manually control withdrawal approval/denial
        """
        return {
            'approve': self.approve_withdrawal,  # Rarely used
            'deny_with_barrier': self.apply_barrier,  # Most common
            'partial_approve': self.approve_partial,  # Sometimes for trust
            'delay': self.delay_processing  # Stalling tactic
        }

class VictimCRM:
    def create_victim_profile(self, initial_contact_data):
        profile = {
            # Basic Information
            'victim_id': generate_unique_id(),
            'real_name': initial_contact_data.name,
            'age': initial_contact_data.age,
            'location': initial_contact_data.location,
            'contact_info': initial_contact_data.contacts,
            
            # Financial Assessment
            'estimated_net_worth': None,  # Discovered through conversation
            'income_level': None,
            'liquidity': None,
            'financial_sophistication': None,
            'investment_experience': None,
            
            # Psychological Profile
            'emotional_vulnerabilities': [],
            'manipulation_susceptibility': None,
            'trust_level': 0,
            'attachment_style': None,
            'loneliness_score': None,
            
            # Operational Data
            'first_contact_date': datetime.now(),
            'current_phase': 'initial_contact',
            'assigned_operator': None,
            'assigned_persona': None,
            'total_deposits': 0,
            'withdrawal_attempts': 0,
            
            # Risk Assessment
            'suspicion_level': 0,
            'law_enforcement_risk': 'low',
            'early_warning_indicators': [],
            
            # Targeting Intelligence
            'source_platform': initial_contact_data.platform,
            'conversation_history': [],
            'response_patterns': {},
            'optimal_contact_times': [],
            'effective_tactics': []
        }
        
        return profile
    
    def update_financial_assessment(self, victim_id, conversation_data):
        """
        Extract financial information from conversations
        """
        financial_indicators = {
            'mentions_salary': self.extract_income_info(conversation_data),
            'mentions_savings': self.extract_savings_info(conversation_data),
            'mentions_assets': self.extract_asset_info(conversation_data),
            'mentions_debt': self.extract_debt_info(conversation_data),
            'employment_stability': self.assess_employment(conversation_data)
        }
        
        # Calculate estimated extractable amount
        estimated_capacity = self.calculate_victim_capacity(financial_indicators)
        
        self.update_profile(victim_id, {
            'estimated_max_capacity': estimated_capacity,
            'financial_indicators': financial_indicators
        })

class VictimBehaviorAnalytics:
    def analyze_victim_behavior(self, victim_id):
        """
        Track and analyze victim behavior patterns
        """
        victim = self.get_victim(victim_id)
        
        analytics = {
            'engagement_score': self.calculate_engagement(victim),
            'emotional_attachment': self.measure_attachment(victim),
            'financial_disclosure_rate': self.measure_openness(victim),
            'resistance_patterns': self.identify_resistance(victim),
            'optimal_messaging_times': self.find_best_contact_times(victim),
            'successful_tactics': self.rank_effective_tactics(victim),
            'risk_of_disengagement': self.predict_dropout_risk(victim)
        }
        
        return analytics
    
    def predict_optimal_investment_amount(self, victim_id):
        """
        Determine the right investment amount to suggest
        """
        victim = self.get_victim(victim_id)
        
        factors = {
            'trust_level': victim.trust_level,
            'disclosed_resources': victim.estimated_max_capacity,
            'current_deposits': victim.total_deposits,
            'risk_tolerance': victim.psychological_profile.risk_tolerance,
            'time_in_relationship': (datetime.now() - victim.first_contact_date).days
        }
        
        # Start small, increase gradually
        if victim.total_deposits == 0:
            # First investment: comfortable but meaningful
            suggested_amount = min(1000, victim.estimated_max_capacity * 0.05)
        else:
            # Subsequent investments: escalate based on comfort
            suggested_amount = victim.average_deposit * 1.5
        
        return suggested_amount

class OperatorManagementSystem:
    def assign_victim_to_operator(self, victim_id):
        """
        Intelligent assignment of victims to operators
        """
        victim = self.get_victim(victim_id)
        available_operators = self.get_available_operators()
        
        # Match based on expertise and capacity
        optimal_operator = self.select_operator(
            victim_profile=victim,
            operator_pool=available_operators,
            criteria=[
                'language_match',
                'cultural_compatibility',
                'experience_level',
                'current_workload',
                'success_rate_with_similar_victims'
            ]
        )
        
        return optimal_operator
    
    def manage_operator_handoffs(self, victim_id, reason):
        """
        Transfer victim between operators (phase transitions)
        """
        # Different operators specialize in different phases
        phase_mapping = {
            'initial_contact': 'junior_operators',
            'relationship_building': 'mid_level_operators',
            'investment_phase': 'senior_operators',
            'escalation': 'specialist_operators',
            'retention': 'expert_operators'
        }
        
        victim = self.get_victim(victim_id)
        required_expertise = phase_mapping[victim.current_phase]
        
        new_operator = self.find_operator_with_expertise(required_expertise)
        
        # Detailed handoff briefing
        handoff_package = self.create_handoff_briefing(victim)
        
        self.transfer_victim(victim_id, new_operator, handoff_package)

class DatingPlatformSecurity:
    def __init__(self):
        self.ai_image_detector = AIGeneratedImageDetector()
        self.device_intelligence = DeviceFingerprinting()
        self.behavioral_analysis = BehavioralBiometrics()
        self.document_verification = IDVerificationService()
    
    def verify_new_user(self, user_registration_data):
        """
        Multi-layer verification for new user accounts
        """
        verification_layers = {}
        
        # Layer 1: AI-generated image detection
        if user_registration_data.profile_photos:
            for photo in user_registration_data.profile_photos:
                verification_layers['photo_authenticity'] = \
                    self.ai_image_detector.analyze(photo)
                
                if verification_layers['photo_authenticity'].ai_generated_probability > 0.7:
                    return VerificationResult(
                        approved=False,
                        reason="Suspicious profile photos detected",
                        required_action="Submit government ID for verification"
                    )
        
        # Layer 2: Device intelligence
        verification_layers['device_trust'] = \
            self.device_intelligence.assess(user_registration_data.device_info)
        
        if verification_layers['device_trust'].risk_score > DEVICE_RISK_THRESHOLD:
            return VerificationResult(
                approved=False,
                reason="Suspicious device characteristics",
                required_action="Additional verification required"
            )
        
        # Layer 3: Behavioral biometrics during registration
        verification_layers['behavior_patterns'] = \
            self.behavioral_analysis.analyze_registration_behavior(
                user_registration_data.interaction_patterns
            )
        
        # Layer 4: Optional government ID verification
        if self.requires_id_verification(user_registration_data):
            verification_layers['identity_documents'] = \
                self.document_verification.verify(user_registration_data.documents)
        
        # Calculate overall risk score
        overall_risk = self.calculate_risk_score(verification_layers)
        
        return VerificationResult(
            approved=overall_risk < APPROVAL_THRESHOLD,
            risk_score=overall_risk,
            verification_layers=verification_layers
        )

class ScamBehaviorDetector:
    def monitor_user_messages(self, user_id, outgoing_messages):
        """
        Detect scam indicators in user communications
        """
        scam_indicators = {
            'investment_mentions': self.detect_investment_discussion(outgoing_messages),
            'financial_requests': self.detect_money_requests(outgoing_messages),
            'platform_switching': self.detect_off_platform_movement(outgoing_messages),
            'rapid_intimacy': self.detect_love_bombing(outgoing_messages),
            'wealth_displays': self.detect_wealth_signaling(outgoing_messages)
        }
        
        risk_score = self.calculate_scam_risk(scam_indicators)
        
        if risk_score > HIGH_RISK_THRESHOLD:
            # Take protective action
            self.flag_account_for_review(user_id)
            self.warn_conversation_partners(user_id)
            self.escalate_to_security_team(user_id, scam_indicators)
        
        return risk_score
    
    def detect_investment_discussion(self, messages):
        """
        Identify investment/trading related conversations
        """
        investment_keywords = [
            'cryptocurrency', 'bitcoin', 'forex', 'trading',
            'investment platform', 'profit', 'returns',
            'passive income', 'financial freedom'
        ]
        
        keyword_frequency = self.count_keywords(messages, investment_keywords)
        
        return InvestmentDiscussionScore(
            frequency=keyword_frequency,
            risk_level='high' if keyword_frequency > 5 else 'low'
        )
    
    def detect_off_platform_movement(self, messages):
        """
        Detect attempts to move conversation off platform
        """
        off_platform_indicators = [
            'whatsapp', 'telegram', 'wechat', 'line',
            'my number is', 'call me at', 'add me on',
            'email me', 'contact me at'
        ]
        
        movement_attempts = self.count_keywords(messages, off_platform_indicators)
        
        # Legitimate users occasionally share contacts
        # but rapid movement off-platform is suspicious
        if movement_attempts > 0 and self.message_count(messages) < 10:
            return PlatformMovementRisk(
                detected=True,
                risk_level='high',
                reason='Early off-platform movement attempt'
            )
        
        return PlatformMovementRisk(detected=False, risk_level='low')

class UserProtectionSystem:
    def display_contextual_warnings(self, conversation_context):
        """
        Show warnings when scam indicators are detected
        """
        if self.detects_investment_discussion(conversation_context):
            return Warning(
                level='high',
                message="""
                ⚠️ Investment Scam Warning
                
                This conversation mentions investments or trading platforms.
                Please be aware:
                
                • Romance scam criminals often pose as successful investors
                • Fake trading platforms steal your money
                • Never send money to someone you haven't met in person
                • Investment opportunities from dating app matches are red flags
                
                If you're asked to invest, report this profile immediately.
                """,
                actions=['report_profile', 'block_user', 'learn_more']
            )
        
        if self.detects_off_platform_movement(conversation_context):
            return Warning(
                level='medium',
                message="""
                🛡️ Safety Reminder
                
                This person wants to move your conversation off our platform.
                
                For your safety:
                • Keep conversations on the app where we can protect you
                • Moving off-platform removes safety protections
                • Scammers often request off-platform communication
                
                Proceed with caution.
                """,
                actions=['stay_on_platform', 'report_if_suspicious']
            )

class FinancialPlatformValidation:
    def validate_trading_platform(self, platform_url):
        """
        Verify legitimacy of trading platforms
        """
        validation_checks = {
            'regulatory_registration': self.check_regulatory_status(platform_url),
            'domain_age': self.check_domain_history(platform_url),
            'ssl_certificate': self.validate_ssl_cert(platform_url),
            'company_registration': self.verify_company_exists(platform_url),
            'user_reviews': self.analyze_user_reviews(platform_url),
            'known_scam_database': self.check_scam_lists(platform_url)
        }
        
        legitimacy_score = self.calculate_legitimacy(validation_checks)
        
        return ValidationResult(
            legitimate=legitimacy_score > LEGITIMACY_THRESHOLD,
            score=legitimacy_score,
            checks=validation_checks,
            warnings=self.generate_warnings(validation_checks)
        )
    
    def check_regulatory_status(self, platform_url):
        """
        Verify platform is registered with financial regulators
        """
        regulators = [
            'SEC',  # US Securities and Exchange Commission
            'CFTC',  # US Commodity Futures Trading Commission
            'FCA',  # UK Financial Conduct Authority
            'ASIC',  # Australian Securities and Investments Commission
            'MAS',  # Monetary Authority of Singapore
        ]
        
        registrations = []
        for regulator in regulators:
            is_registered = self.query_regulator_database(platform_url, regulator)
            registrations.append({
                'regulator': regulator,
                'registered': is_registered
            })
        
        # Legitimate platforms are registered with at least one major regulator
        any_registration = any(r['registered'] for r in registrations)
        
        return RegulatoryStatus(
            registered=any_registration,
            registrations=registrations,
            risk_level='critical' if not any_registration else 'low'
        )

class FinancialLiteracySystem:
    def provide_investment_education(self, user_context):
        """
        Educate users about investment fraud
        """
        if user_context.first_time_investor:
            return EducationalModule(
                title="Before You Invest: Essential Safety Tips",
                content="""
                🎓 Investment Safety Essentials
                
                Before investing, always verify:
                
                1. Regulatory Registration
                   • Check SEC, FINRA, or your country's regulator
                   • Legitimate platforms are always registered
                   • Registration numbers should be verifiable
                
                2. Warning Signs of Fraud
                   • Guaranteed returns or "no risk" promises
                   • Pressure to invest quickly
                   • Difficulty withdrawing funds
                   • "Secret" investment strategies
                   • Unusual payment methods (cryptocurrency only)
                
                3. Due Diligence Steps
                   • Research company history and reviews
                   • Verify physical address and contact info
                   • Start with small amounts
                   • Never invest more than you can afford to lose
                   
                4. Red Flags
                   • Platform found through dating app or social media
                   • Recommended by online romantic interest
                   • No phone support or physical office
                   • Website is very new (less than 6 months old)
                
                💡 If something seems too good to be true, it probably is.
                """,
                quiz=self.create_knowledge_check_quiz(),
                resources=self.get_educational_resources()
            )
    
    def show_withdrawal_difficulty_warning(self, withdrawal_request):
        """
        Warn users about common fraud tactics
        """
        if withdrawal_request.first_withdrawal:
            return Alert(
                level='info',
                message="""
                💡 Withdrawal Best Practices
                
                Legitimate platforms allow easy withdrawals.
                
                Warning signs of fraud:
                • Requests to pay "taxes" or "fees" before withdrawal
                • Need to deposit more money to "verify" account
                • Account "upgrades" required for withdrawals
                • Prolonged delays or technical excuses
                • Complex verification processes not mentioned before
                
                If you experience these issues, stop immediately and report.
                """,
                actions=['report_suspicious_platform', 'get_help']
            )

class CommunicationPlatformSecurity:
    def moderate_messages(self, message_data):
        """
        Detect and moderate scam-related content
        """
        moderation_checks = {
            'investment_solicitation': self.detect_investment_pitch(message_data),
            'romance_scam_indicators': self.detect_romance_scam_patterns(message_data),
            'external_platform_links': self.detect_suspicious_links(message_data),
            'financial_urgency': self.detect_pressure_tactics(message_data)
        }
        
        if any(check.risk_level == 'high' for check in moderation_checks.values()):
            return ModerationAction(
                action='flag_and_warn',
                warn_recipient=True,
                flag_sender=True,
                escalate_to_human=True
            )
        
        return ModerationAction(action='allow')
    
    def detect_suspicious_links(self, message_data):
        """
        Identify potentially malicious platform links
        """
        links = self.extract_links(message_data)
        
        suspicious_indicators = []
        for link in links:
            checks = {
                'domain_age': self.check_domain_age(link),
                'ssl_certificate': self.validate_certificate(link),
                'blacklist_status': self.check_blacklists(link),
                'trading_platform_keywords': self.analyze_content(link)
            }
            
            if self.is_suspicious(checks):
                suspicious_indicators.append({
                    'link': link,
                    'reasons': checks
                })
        
        return SuspiciousLinksResult(
            found=len(suspicious_indicators) > 0,
            suspicious_links=suspicious_indicators,
            risk_level='high' if suspicious_indicators else 'low'
        )

class UserReportingSystem:
    def process_scam_report(self, report_data):
        """
        Handle user reports of suspected scams
        """
        # Immediate actions
        immediate_actions = {
            'suspend_reported_account': self.suspend_account(report_data.reported_user_id),
            'warn_other_victims': self.identify_and_warn_other_victims(report_data.reported_user_id),
            'preserve_evidence': self.collect_conversation_evidence(report_data),
            'notify_authorities': self.report_to_law_enforcement(report_data)
        }
        
        # Investigation
        investigation = self.conduct_investigation(report_data)
        
        # Permanent action based on findings
        if investigation.confirms_scam:
            self.permanently_ban_account(report_data.reported_user_id)
            self.ban_associated_accounts(investigation.related_accounts)
            self.share_with_industry_databases(investigation.findings)
        
        # Support for reporter
        self.provide_victim_resources(report_data.reporter_id)
        
        return ReportResolution(
            actions_taken=immediate_actions,
            investigation_results=investigation,
            reporter_support=self.get_support_resources()
        )
    
    def identify_and_warn_other_victims(self, scammer_id):
        """
        Find and warn other users who have interacted with confirmed scammer
        """
        potential_victims = self.get_conversation_partners(scammer_id)
        
        for victim_id in potential_victims:
            self.send_warning_message(victim_id, template="""
                ⚠️ Important Security Alert
                
                We've identified that you recently communicated with an account
                involved in investment fraud.
                
                For your protection:
                • Do NOT send money or invest through any platforms they recommended
                • If you already invested, contact us immediately for assistance
                • Report any suspicious activity
                
                We're here to help. Contact our support team 24/7.
                
                Resources:
                • Report fraud: [link]
                • Get support: [link]
                • Learn about scams: [link]
                """)

class IndustryCollaborationFramework:
    def __init__(self):
        self.shared_intel_database = IndustryIntelligenceDB()
        self.reporting_api = CrossPlatformReportingAPI()
    
    def report_confirmed_scammer(self, scammer_data):
        """
        Share confirmed scammer information with industry partners
        """
        intel_report = {
            'scammer_identifiers': {
                'user_ids': scammer_data.user_ids,
                'email_addresses': self.hash_pii(scammer_data.emails),
                'phone_numbers': self.hash_pii(scammer_data.phones),
                'device_fingerprints': scammer_data.device_prints,
                'ip_addresses': scammer_data.ip_addresses,
                'cryptocurrency_wallets': scammer_data.crypto_wallets
            },
            'scam_details': {
                'scam_type': 'pig_butchering',
                'platforms_used': scammer_data.platforms,
                'fake_trading_platforms': scammer_data.fraudulent_sites,
                'victim_count': scammer_data.victim_count,
                'total_loss_amount': scammer_data.total_losses
            },
            'evidence': {
                'conversation_samples': scammer_data.message_samples,
                'fake_profile_images': scammer_data.profile_photos,
                'platform_screenshots': scammer_data.platform_screenshots
            },
            'reporting_organization': self.organization_id,
            'timestamp': datetime.now(),
            'confidence_level': scammer_data.confidence_score
        }
        
        # Share with industry partners
        self.shared_intel_database.add_entry(intel_report)
        self.reporting_api.broadcast_alert(intel_report)
        
        # Report to law enforcement
        self.report_to_authorities(intel_report)
    
    def check_against_industry_intel(self, user_data):
        """
        Check new users against shared intelligence database
        """
        matches = self.shared_intel_database.query({
            'email_hash': self.hash_pii(user_data.email),
            'phone_hash': self.hash_pii(user_data.phone),
            'device_fingerprint': user_data.device_fingerprint,
            'ip_address': user_data.ip_address
        })
        
        if matches:
            return IntelligenceMatch(
                found=True,
                match_details=matches,
                recommended_action='block_and_investigate'
            )
        
        return IntelligenceMatch(found=False)