def calculate_edd_score(client_data):
    base_score = 0.0
 
    # 1. Risque géographique (40% du score)
    geo_risk = assess_geographical_risk(
        client_data.nationality,
        client_data.residence,
        client_data.business_countries
    )
    base_score += geo_risk * 0.4
 
    # 2. Risque PEP/sanctions (35% du score)
    person_risk = screen_sanctions_pep(
        client_data.name,
        client_data.birth_date,
        client_data.associates
    )
    base_score += person_risk * 0.35
 
    # 3. Risque activité (15% du score)
    activity_risk = assess_business_risk(
        client_data.activity_code,
        client_data.turnover,
        client_data.business_model
    )
    base_score += activity_risk * 0.15
 
    # 4. Risque structurel (10% du score)
    structure_risk = assess_structure_complexity(
        client_data.ownership_chain,
        client_data.ubo_levels,
        client_data.jurisdictions
    )
    base_score += structure_risk * 0.10
 
    return min(base_score, 1.0)  # Cap à 1.0
 
# Seuils de déclenchement
def requires_edd(score, segment="cgp"):
    thresholds = {
        "cgp": 0.3,      # Plus conservateur
        "fintech": 0.4,  # Volume élevé, seuil plus permissif
        "asset_mgmt": 0.2 # Très conservateur (réglementaire strict)
    }
    return score >= thresholds.get(segment, 0.3)

# Exemple d'indicateurs ML
suspicious_patterns = {
    "sudden_volume_increase": {
        "description": "Augmentation soudaine >300% volume mensuel",
        "weight": 0.25,
        "threshold": 0.7
    },
    "unusual_timing": {
        "description": "Transactions en dehors heures ouvrables",
        "weight": 0.15,
        "threshold": 0.6
    },
    "geographical_inconsistency": {
        "description": "Transactions depuis pays inattendus",
        "weight": 0.30,
        "threshold": 0.8
    },
    "counterpart_risk": {
        "description": "Transactions avec entités à risque",
        "weight": 0.30,
        "threshold": 0.5
    }
}

graph TD
    A[Client détecté à risque] --> B{Score EDD > 0.3?}
    B -->|Non| C[KYC standard]
    B -->|Oui| D[Déclenchement EDD automatique]
 
    D --> E[Collecte documents renforcée]
    E --> F[Analyse automatisée]
    F --> G[Validation conseiller]
    G --> H[Export dossier conformité]
 
    I[Surveillance continue] --> J{Alerte détectée?}
    J -->|Oui| D
    J -->|Non| K[Revue périodique]

// Interface simplifiée pour CGP
const EDDDecisionSupport = {
  clientRisk: "ÉLEVÉ",
  triggerReasons: [
    "Résidence fiscale : Monaco (pays à risque fiscal)",
    "Activité : Import/Export (secteur sensible)",
    "Transaction inhabituelle : 50k€ vs moyenne 5k€"
  ],
  recommendedActions: [
    "✅ Demander justificatifs revenus 2 dernières années",
    "✅ Vérifier source des fonds transaction 50k€",
    "✅ Documenter raisons économiques opération"
  ],
  complianceLevel: "CONFORME" // Automated assessment
}

# API endpoint pour déclenchement EDD
@app.post("/api/v1/clients/{client_id}/edd-assessment")
async def trigger_edd_assessment(
    client_id: str,
    trigger_data: EDDTriggerData,
    background_tasks: BackgroundTasks
):
    # 1. Calcul du score de risque
    risk_score = await calculate_risk_score(client_id, trigger_data)
 
    # 2. Décision automatisée
    if risk_score >= EDD_THRESHOLD:
        # Déclenchement workflow EDD
        edd_case = await create_edd_case(client_id, risk_score)
 
        # Notification temps réel
        background_tasks.add_task(
            notify_compliance_team,
            edd_case.id,
            priority="high" if risk_score > 0.8 else "medium"
        )
 
        # Webhook client
        await send_webhook(
            client_webhook_url,
            {
                "event": "edd_triggered",
                "client_id": client_id,
                "case_id": edd_case.id,
                "next_actions": edd_case.required_actions
            }
        )
 
    return {
        "requires_edd": risk_score >= EDD_THRESHOLD,
        "risk_score": risk_score,
        "case_id": edd_case.id if risk_score >= EDD_THRESHOLD else None
    }

def edd_asset_management(investor_data, fund_type):
    edd_requirements = []
 
    # Obligations selon type de fonds
    if fund_type in ["OPCVM", "FIA_grand_public"]:
        edd_requirements.extend([
            "identity_verification_enhanced",
            "source_of_funds_documentation",
            "beneficial_ownership_mapping"
        ])
 
    elif fund_type == "private_equity":
        edd_requirements.extend([
            "institutional_due_diligence",
            "co_investors_screening",
            "fund_structure_analysis",
            "regulatory_approvals_check"
        ])
 
    # EDD renforcée si investisseur >10% du fonds
    if investor_data.investment_percentage > 10:
        edd_requirements.extend([
            "enhanced_ubo_investigation",
            "board_members_screening",
            "related_entities_mapping"
        ])
 
    return edd_requirements

# 1. Assessment initial du risque
POST /api/v1/edd/risk-assessment
{
  "client_id": "client_123456",
  "trigger_event": "account_opening|transaction|periodic_review",
  "client_data": {
    "personal_info": {...},
    "business_info": {...},
    "transaction_data": {...}
  }
}
 
# 2. Déclenchement workflow EDD
POST /api/v1/edd/cases
{
  "client_id": "client_123456",
  "risk_score": 0.75,
  "trigger_reasons": ["pep_identified", "high_risk_country"],
  "urgency": "high|medium|low"
}
 
# 3. Collecte documents renforcée
POST /api/v1/edd/cases/{case_id}/documents
{
  "document_type": "source_of_funds|wealth_declaration|business_rationale",
  "file_url": "https://...",
  "metadata": {...}
}
 
# 4. Statut et progression
GET /api/v1/edd/cases/{case_id}/status
{
  "status": "pending|in_progress|under_review|approved|rejected",
  "completion_percentage": 75,
  "missing_elements": [...],
  "estimated_completion": "2025-01-20T10:00:00Z"
}

graph TB
    subgraph "EDD Orchestrator"
        A[Risk Assessment Engine]
        B[Workflow Manager]
        C[Document Processor]
        D[Decision Engine]
    end
 
    subgraph "External Sources"
        E[Sanctions/PEP APIs]
        F[Company Registries]
        G[Adverse Media]
        H[Government DBs]
    end
 
    subgraph "Client Systems"
        I[CRM Integration]
        J[Core Banking]
        K[Compliance Dashboard]
        L[Audit Trail DB]
    end
 
    A --> B
    B --> C
    C --> D
 
    E --> A
    F --> A
    G --> A
    H --> A
 
    D --> I
    D --> J
    D --> K
    D --> L

// Trigger Salesforce pour déclenchement EDD automatique
trigger EDDAssessmentTrigger on Account (after update) {
    for (Account acc : Trigger.new) {
        Account oldAcc = Trigger.oldMap.get(acc.Id);
 
        // Vérification critères déclencheurs
        Boolean requiresEDD = false;
 
        // Changement pays de résidence vers pays à risque
        if (acc.Country__c != oldAcc.Country__c &&
            HighRiskCountries.contains(acc.Country__c)) {
            requiresEDD = true;
        }
 
        // Augmentation patrimoine significative
        if (acc.Total_Assets__c > oldAcc.Total_Assets__c * 1.5 &&
            acc.Total_Assets__c > 100000) {
            requiresEDD = true;
        }
 
        if (requiresEDD) {
            // Appel API Swiftgum
            EDDAssessmentService.triggerEDD(acc.Id);
        }
    }
}

async def process_edd_document(document_upload):
    # 1. Validation technique
    technical_check = await validate_document_format(document_upload)
    if not technical_check.valid:
        return {"status": "rejected", "reason": technical_check.error}
 
    # 2. Extraction des données (OCR/NLP)
    extracted_data = await extract_document_data(document_upload)
 
    # 3. Vérification cohérence
    consistency_score = await check_data_consistency(
        extracted_data,
        client_declared_info
    )
 
    # 4. Scoring de confiance
    confidence_score = await calculate_document_confidence(
        document_upload,
        extracted_data,
        external_sources
    )
 
    # 5. Décision automatique ou escalade
    if confidence_score > 0.8 and consistency_score > 0.9:
        decision = "auto_approved"
    elif confidence_score < 0.3 or consistency_score < 0.5:
        decision = "auto_rejected"
    else:
        decision = "human_review_required"
 
    return {
        "status": decision,
        "confidence": confidence_score,
        "consistency": consistency_score,
        "extracted_data": extracted_data,
        "next_action": get_next_action(decision)
    }

{
  "edd_case_id": "EDD-2025-001234",
  "client_id": "CLIENT-789456",
  "timeline": [
    {
      "timestamp": "2025-01-15T09:00:00Z",
      "event_type": "case_created",
      "trigger": "pep_identified",
      "risk_score": 0.75,
      "operator": "system_automated",
      "details": {
        "pep_match": {
          "name": "Jean MARTIN",
          "function": "Former Minister",
          "country": "France",
          "confidence": 0.95
        }
      }
    },
    {
      "timestamp": "2025-01-15T09:15:00Z",
      "event_type": "document_requested",
      "document_types": ["cv_detailed", "wealth_declaration"],
      "notification_sent": "email + sms",
      "operator": "system_automated"
    },
    {
      "timestamp": "2025-01-16T14:30:00Z",
      "event_type": "document_received",
      "document_id": "DOC-CV-001",
      "validation_result": {
        "technical_check": "passed",
        "consistency_score": 0.92,
        "confidence_score": 0.87,
        "decision": "auto_approved"
      },
      "operator": "system_automated"
    },
    {
      "timestamp": "2025-01-17T11:00:00Z",
      "event_type": "human_review",
      "reviewer": "marie.compliance@cgp.fr",
      "decision": "approved",
      "rationale": "Documentation complète, cohérence vérifiée, source de fonds justifiée",
      "risk_mitigation": ["periodic_review_quarterly", "transaction_monitoring_enhanced"]
    }
  ],
  "final_decision": {
    "status": "approved",
    "risk_level": "medium_high",
    "mitigation_measures": [
      "enhanced_monitoring",
      "quarterly_review",
      "transaction_threshold_10k"
    ],
    "next_review_date": "2025-04-17",
    "approver": "marie.compliance@cgp.fr",
    "approval_date": "2025-01-17T11:30:00Z"
  }
}

integrations:
  crm:
    - name: "Salesforce Financial Services"
      type: "bidirectional_sync"
      fields: ["edd_status", "risk_score", "next_review"]
 
    - name: "Microsoft Dynamics 365"
      type: "webhook_events"
      events: ["edd_triggered", "document_received", "case_closed"]
 
  gestion_patrimoine:
    - name: "CGPI Suite"
      type: "api_integration"
      use_case: "client_risk_profiling"
 
    - name: "Harvest"
      type: "data_export"
      frequency: "daily"
 
  communication:
    - name: "Selligent Campaign"
      type: "automated_campaigns"
      use_case: "document_collection_reminders"

integrations:
  core_banking:
    - name: "Mambu Core"
      type: "real_time_api"
      events: ["account_opening", "transaction_alert"]
 
    - name: "Custom Core Banking"
      type: "webhook_integration"
      security: "jwt_authentication"
 
  compliance_tools:
    - name: "ComplyAdvantage"
      type: "enhanced_screening"
      use_case: "adverse_media_monitoring"
 
    - name: "Refinitiv World-Check"
      type: "pep_sanctions_screening"
      sla: "sub_second_response"
 
  observability:
    - name: "Datadog"
      type: "metrics_logging"
      dashboards: ["edd_performance", "sla_monitoring"]
 
    - name: "Grafana"
      type: "custom_dashboards"
      alerts: ["high_false_positive_rate", "sla_breach"]

EDD_CONFIG = {
    "cgp_cif": {
        "risk_thresholds": {
            "pep_score": 0.3,           # Conservateur
            "sanctions_score": 0.0,     # Zero tolerance
            "geo_risk_score": 0.4,      # Modéré
            "transaction_unusual": 0.6   # Relativement permissif
        },
        "document_requirements": {
            "pep": ["cv_detailed", "wealth_source", "conflict_attestation"],
            "geo_risk": ["fiscal_residence", "business_license"],
            "transaction": ["economic_rationale", "supporting_contracts"]
        },
        "review_frequency": {
            "high_risk": "quarterly",
            "medium_risk": "semi_annual",
            "low_risk": "annual"
        }
    },
 
    "fintech_psp": {
        "risk_thresholds": {
            "pep_score": 0.4,           # Moins conservateur (volume)
            "sanctions_score": 0.0,     # Zero tolerance
            "geo_risk_score": 0.5,      # Plus permissif
            "transaction_unusual": 0.7   # Machine learning tuned
        },
        "automation_level": {
            "auto_approve": 85,         # % cas auto-approuvés
            "auto_reject": 5,           # % cas auto-rejetés
            "human_review": 10          # % nécessitant revue humaine
        },
        "sla_targets": {
            "initial_assessment": "5_minutes",
            "document_processing": "2_hours",
            "final_decision": "24_hours"
        }
    }
}

semaine_1:
  - Configuration sources INPI/Sirene/RBE
  - Paramétrage seuils segment pilote
  - Tests unitaires API screening
 
semaine_2:
  - Intégration CRM principal
  - Configuration workflows notifications
  - Formation équipe conformité (4h)
 
semaine_3:
  - Tests end-to-end cas types
  - Ajustement seuils basé sur historique
  - Documentation processus
 
semaine_4:
  - Déploiement pilote (20% nouveaux clients)
  - Monitoring KPIs initialisation
  - First feedback & ajustements