app/services/workflow/workflow_engine.py

"""
Workflow Automation Engine - Comprehensive investigation automation
"""

import logging
from datetime import datetime, timedelta
from enum import Enum
from typing import Any

from pydantic import BaseModel

logger = logging.getLogger(__name__)


class CaseType(str, Enum):
    AML_INVESTIGATION = "aml_investigation"
    FRAUD_DETECTION = "fraud_detection"
    NETWORK_ANALYSIS = "network_analysis"
    COMPLIANCE_REVIEW = "compliance_review"


class InvestigationStatus(str, Enum):
    NEW = "new"
    IN_PROGRESS = "in_progress"
    EVIDENCE_COLLECTION = "evidence_collection"
    ANALYSIS = "analysis"
    REVIEW = "review"
    COMPLETED = "completed"
    CLOSED = "closed"


class AutomatedAction(BaseModel):
    """Automated action generated for investigation workflow"""

    id: str
    action_type: str
    title: str
    description: str
    priority: str  # low, medium, high, critical
    evidence_required: list[str]
    estimated_duration: int  # in minutes
    ai_persona: str
    confidence_score: float
    execution_script: str | None = None


class InvestigationTemplate(BaseModel):
    """Auto-generated investigation template"""

    id: str
    case_type: CaseType
    title: str
    description: str
    required_evidence: list[str]
    standard_actions: list[str]
    regulatory_requirements: list[str]
    estimated_duration: int  # in hours


class WorkflowEngine:
    """Automated investigation workflow engine"""

    def __init__(self):
        self.case_templates = self._initialize_templates()
        self.workflow_rules = self._initialize_workflow_rules()

    def _initialize_templates(self) -> dict[CaseType, InvestigationTemplate]:
        """Initialize investigation templates for different case types"""
        return {
            CaseType.AML_INVESTIGATION: InvestigationTemplate(
                id="aml_template_001",
                case_type=CaseType.AML_INVESTIGATION,
                title="Anti-Money Laundering Investigation",
                description="Comprehensive AML investigation template covering transaction analysis, structuring detection, and SAR filing",
                required_evidence=[
                    "Transaction records",
                    "Account statements",
                    "Customer identification documents",
                    "Wire transfer instructions",
                    "Beneficial ownership documentation",
                ],
                standard_actions=[
                    "Initial risk assessment",
                    "Transaction pattern analysis",
                    "Network mapping",
                    "Evidence collection",
                    "SAR preparation and filing",
                ],
                regulatory_requirements=[
                    "Fincen 314a requirements",
                    "FATF 40 recommendations",
                    "Local AML regulations",
                ],
                estimated_duration=48,
            ),
            CaseType.FRAUD_DETECTION: InvestigationTemplate(
                id="fraud_template_001",
                case_type=CaseType.FRAUD_DETECTION,
                title="Fraud Detection Investigation",
                description="Template for investigating various types of fraud including account takeover, payment fraud, and identity theft",
                required_evidence=[
                    "Suspicious activity logs",
                    "Account access records",
                    "Device fingerprinting data",
                    "Customer complaints",
                    "Transaction patterns",
                ],
                standard_actions=[
                    "Fraud assessment",
                    "Evidence gathering",
                    "Victim interview",
                    "Forensic analysis",
                    "Loss calculation",
                ],
                regulatory_requirements=[
                    "Consumer protection laws",
                    "Data breach notification",
                    "Financial institution policies",
                ],
                estimated_duration=24,
            ),
            CaseType.NETWORK_ANALYSIS: InvestigationTemplate(
                id="network_template_001",
                case_type=CaseType.NETWORK_ANALYSIS,
                title="Transaction Network Analysis",
                description="Template for analyzing complex transaction networks and identifying connected entities",
                required_evidence=[
                    "Transaction data",
                    "Entity relationship data",
                    "Geographic information",
                    "Communication patterns",
                ],
                standard_actions=[
                    "Network mapping",
                    "Entity identification",
                    "Relationship analysis",
                    "Flow tracing",
                    "Visualization",
                ],
                regulatory_requirements=[
                    "Data privacy regulations",
                    "Cross-border transfer rules",
                    "Sanctions screening requirements",
                ],
                estimated_duration=72,
            ),
            CaseType.COMPLIANCE_REVIEW: InvestigationTemplate(
                id="compliance_template_001",
                case_type=CaseType.COMPLIANCE_REVIEW,
                title="Regulatory Compliance Review",
                description="Template for conducting comprehensive compliance reviews and audit preparation",
                required_evidence=[
                    "Policy documents",
                    "Transaction samples",
                    "Customer risk assessments",
                    "Internal controls documentation",
                ],
                standard_actions=[
                    "Policy review",
                    "Gap analysis",
                    "Control testing",
                    "Audit preparation",
                    "Management reporting",
                ],
                regulatory_requirements=[
                    "Applicable regulations",
                    "Industry standards",
                    "Best practices framework",
                ],
                estimated_duration=36,
            ),
        }

    def _initialize_workflow_rules(self) -> dict[str, Any]:
        """Initialize workflow automation rules"""
        return {
            "auto_escalation": {
                "risk_threshold": 0.8,
                "time_threshold": 24,  # hours
                "required_approval": True,
            },
            "evidence_collection": {
                "auto_categorization": True,
                "relevance_scoring": True,
                "duplicate_detection": True,
            },
            "timeline_generation": {
                "auto_sorting": True,
                "gap_detection": True,
                "confidence_threshold": 0.7,
            },
            "report_generation": {
                "auto_population": True,
                "template_matching": True,
                "compliance_check": True,
            },
        }

    async def generate_investigation_case(
        self,
        alert_data: dict[str, Any],
        case_type: CaseType,
        ai_insights: list[dict[str, Any]] | None = None,
    ) -> dict[str, Any]:
        """Generate complete investigation case with automation"""
        try:
            template = self.case_templates[case_type]

            # Generate case ID
            case_id = f"case_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

            # Auto-generate investigation actions
            actions = await self._generate_automated_actions(
                alert_data, template, ai_insights
            )

            # Generate evidence collection plan
            evidence_plan = await self._generate_evidence_plan(alert_data, template)

            # Generate timeline
            timeline = await self._generate_investigation_timeline(alert_data, actions)

            # Calculate investigation parameters
            investigation_params = await self._calculate_investigation_parameters(
                alert_data, template, actions
            )

            return {
                "case_id": case_id,
                "template": template.dict(),
                "generated_actions": [action.dict() for action in actions],
                "evidence_plan": evidence_plan,
                "timeline": timeline,
                "parameters": investigation_params,
                "status": InvestigationStatus.NEW,
                "created_at": datetime.now().isoformat(),
                "ai_enhanced": True,
            }

        except Exception as e:
            logger.error(f"Failed to generate investigation case: {e}")
            return {"error": str(e), "case_id": None, "status": InvestigationStatus.NEW}

    async def _generate_automated_actions(
        self,
        alert_data: dict[str, Any],
        template: InvestigationTemplate,
        ai_insights: list[dict[str, Any]],
    ) -> list[AutomatedAction]:
        """Generate AI-enhanced automated actions for investigation"""
        actions = []

        # Analyze alert risk level
        risk_score = alert_data.get("risk_score", 0.5)
        alert_data.get("alert_type", "unknown")

        # Generate actions based on template and AI insights
        for i, standard_action in enumerate(template.standard_actions):
            priority = self._calculate_action_priority(standard_action, risk_score, i)
            confidence = self._calculate_action_confidence(standard_action, ai_insights)
            estimated_duration = self._estimate_action_duration(
                standard_action, alert_data
            )

            action = AutomatedAction(
                id=f"action_{i + 1}",
                action_type=standard_action,
                title=standard_action.replace("_", " ").title(),
                description=f"AI-generated {standard_action} for {template.title}",
                priority=priority,
                evidence_required=self._get_required_evidence(
                    standard_action, template
                ),
                estimated_duration=estimated_duration,
                ai_persona=self._get_best_persona_for_action(standard_action),
                confidence_score=confidence,
            )
            actions.append(action)

        return actions

    def _calculate_action_priority(
        self, action: str, risk_score: float, sequence: int
    ) -> str:
        """Calculate priority based on action sequence and risk level"""
        if risk_score >= 0.8:
            return "critical"
        elif risk_score >= 0.6 or sequence <= 2:
            return "high"
        elif sequence <= 5:
            return "medium"
        else:
            return "low"

    def _calculate_action_confidence(
        self, action: str, ai_insights: list[dict[str, Any]]
    ) -> float:
        """Calculate confidence score for action based on AI insights"""
        if not ai_insights:
            return 0.7  # Default confidence

        # Find matching insights for this action
        matching_insights = [
            insight
            for insight in ai_insights
            if action.lower() in insight.get("description", "").lower()
        ]

        if matching_insights:
            # Average confidence from matching insights
            return sum(
                insight.get("confidence", 0.7) for insight in matching_insights
            ) / len(matching_insights)

        return 0.7

    def _estimate_action_duration(self, action: str, alert_data: dict[str, Any]) -> int:
        """Estimate action duration in minutes based on action type and alert complexity"""
        base_durations = {
            "Initial risk assessment": 15,
            "Transaction pattern analysis": 45,
            "Network mapping": 90,
            "Evidence collection": 120,
            "SAR preparation and filing": 60,
            "Fraud assessment": 30,
            "Forensic analysis": 180,
            "Policy review": 45,
            "Audit preparation": 90,
        }

        # Adjust based on alert complexity
        complexity_factor = 1.0
        if alert_data.get("transaction_count", 0) > 100:
            complexity_factor = 1.5
        if alert_data.get("involved_entities", 0) > 10:
            complexity_factor = 1.3
        if len(alert_data.get("alert_types", [])) > 1:
            complexity_factor = 1.4

        action_duration = base_durations.get(action, 60)
        return int(action_duration * complexity_factor)

    def _get_required_evidence(
        self, action: str, template: InvestigationTemplate
    ) -> list[str]:
        """Get required evidence for a specific action"""
        action_evidence_mapping = {
            "Initial risk assessment": ["Transaction records", "Customer profile"],
            "Transaction pattern analysis": [
                "Transaction history",
                "Account statements",
            ],
            "Network mapping": ["Transaction data", "Entity information"],
            "Evidence collection": ["All available evidence", "Supporting documents"],
            "SAR preparation and filing": [
                "Transaction evidence",
                "Analysis results",
                "Risk assessment",
            ],
        }
        return action_evidence_mapping.get(action, ["Supporting documentation"])

    def _get_best_persona_for_action(self, action: str) -> str:
        """Get the best AI persona for a specific action"""
        persona_mapping = {
            "Initial risk assessment": "risk_quantifier",
            "Transaction pattern analysis": "aml_analyst",
            "Network mapping": "network_mapper",
            "Evidence collection": "behavioral_profiler",
            "SAR preparation and filing": "compliance_officer",
            "Fraud assessment": "network_mapper",
            "Forensic analysis": "network_mapper",
        }
        return persona_mapping.get(action, "aml_analyst")

    async def _generate_evidence_plan(
        self, alert_data: dict[str, Any], template: InvestigationTemplate
    ) -> dict[str, Any]:
        """Generate automated evidence collection plan"""
        return {
            "required_evidence": template.required_evidence,
            "auto_collection_methods": self._get_collection_methods(alert_data),
            "evidence_priority_scoring": True,
            "duplicate_detection": True,
            "metadata_extraction": True,
            "estimated_collection_time": len(template.required_evidence)
            * 30,  # 30 minutes per evidence item
        }

    def _get_collection_methods(self, alert_data: dict[str, Any]) -> list[str]:
        """Get evidence collection methods based on alert data"""
        methods = []

        if alert_data.get("has_transactions"):
            methods.append("Automated transaction parsing")

        if alert_data.get("has_documents"):
            methods.append("Document OCR and analysis")

        if alert_data.get("has_communications"):
            methods.append("Communication pattern analysis")

        if alert_data.get("digital_footprint"):
            methods.append("Digital forensics and IP tracing")

        return methods if methods else ["Manual evidence collection"]

    async def _generate_investigation_timeline(
        self, alert_data: dict[str, Any], actions: list[AutomatedAction]
    ) -> dict[str, Any]:
        """Generate investigation timeline with automated actions"""
        timeline_events = []

        # Add initial alert
        alert_timestamp = alert_data.get("timestamp", datetime.now())
        timeline_events.append(
            {
                "id": "initial_alert",
                "timestamp": alert_timestamp.isoformat(),
                "event": "Fraud Alert Triggered",
                "type": "alert",
                "duration": 0,
            }
        )

        # Add automated actions
        total_duration = 0
        for action in actions:
            start_time = alert_timestamp + timedelta(minutes=total_duration)
            start_time + timedelta(minutes=action.estimated_duration)

            timeline_events.append(
                {
                    "id": action.id,
                    "timestamp": start_time.isoformat(),
                    "event": action.title,
                    "type": "action",
                    "duration": action.estimated_duration,
                    "persona": action.ai_persona,
                    "confidence": action.confidence_score,
                }
            )

            total_duration += action.estimated_duration

        # Add completion milestone
        completion_time = alert_timestamp + timedelta(minutes=total_duration)
        timeline_events.append(
            {
                "id": "investigation_completion",
                "timestamp": completion_time.isoformat(),
                "event": "Automated Investigation Complete",
                "type": "milestone",
                "duration": 0,
            }
        )

        return {
            "events": timeline_events,
            "total_estimated_duration": total_duration,
            "estimated_completion": completion_time.isoformat(),
            "automation_level": len(actions) / 10,  # Automation score out of 10
        }

    async def _calculate_investigation_parameters(
        self,
        alert_data: dict[str, Any],
        template: InvestigationTemplate,
        actions: list[AutomatedAction],
    ) -> dict[str, Any]:
        """Calculate investigation parameters"""
        return {
            "complexity_score": self._calculate_complexity_score(alert_data),
            "resource_requirements": self._estimate_resources(actions),
            "critical_path_analysis": self._identify_critical_path(actions),
            "success_probability": self._calculate_success_probability(
                alert_data, actions
            ),
            "regulatory_risk_level": self._assess_regulatory_risk(alert_data),
            "automation_potential": len(actions) / len(template.standard_actions) * 100,
        }

    def _calculate_complexity_score(self, alert_data: dict[str, Any]) -> float:
        """Calculate investigation complexity score"""
        score = 0.0

        # Transaction count complexity
        tx_count = alert_data.get("transaction_count", 1)
        if tx_count > 50:
            score += 0.3
        elif tx_count > 100:
            score += 0.6
        elif tx_count > 200:
            score += 1.0

        # Entity complexity
        entity_count = alert_data.get("involved_entities", 1)
        if entity_count > 5:
            score += 0.2
        elif entity_count > 10:
            score += 0.4

        # Time span complexity
        time_span_days = alert_data.get("time_span_days", 1)
        if time_span_days > 30:
            score += 0.3
        elif time_span_days > 90:
            score += 0.6

        return min(score, 1.0)

    def _estimate_resources(self, actions: list[AutomatedAction]) -> dict[str, Any]:
        """Estimate resource requirements for investigation"""
        total_duration = sum(action.estimated_duration for action in actions)
        investigator_hours = total_duration / 60  # Convert to hours

        return {
            "total_estimated_hours": investigator_hours,
            "required_skills": list(
                {self._get_required_skills(action.action_type) for action in actions}
            ),
            "system_requirements": [
                "Investigation Platform",
                "AI Assistant",
                "Data Analysis Tools",
            ],
            "estimated_cost": investigator_hours * 150,  # $150/hour average
            "critical_path_hours": sum(
                action.estimated_duration
                for action in actions
                if action.priority in ["critical", "high"]
            )
            / 60,
        }

    def _get_required_skills(self, action_type: str) -> list[str]:
        """Get required skills for action type"""
        skill_mapping = {
            "Initial risk assessment": ["Risk Analysis", "Quantitative Methods"],
            "Transaction pattern analysis": ["AML Knowledge", "Pattern Recognition"],
            "Network mapping": [
                "Graph Theory",
                "Relationship Analysis",
                "Data Visualization",
            ],
            "Evidence collection": [
                "Forensic Analysis",
                "Evidence Handling",
                "Chain of Custody",
            ],
            "SAR preparation and filing": [
                "Regulatory Compliance",
                "Legal Writing",
                "Documentation",
            ],
            "Fraud assessment": [
                "Fraud Investigation",
                "Interviewing",
                "Digital Forensics",
            ],
        }
        return skill_mapping.get(action_type, ["Investigation"])

    def _identify_critical_path(self, actions: list[AutomatedAction]) -> list[str]:
        """Identify critical path actions"""
        critical_actions = []

        for action in actions:
            if (
                action.priority in ["critical", "high"]
                and action.confidence_score >= 0.8
            ):
                critical_actions.append(action.id)

        return critical_actions

    def _calculate_success_probability(
        self, alert_data: dict[str, Any], actions: list[AutomatedAction]
    ) -> float:
        """Calculate probability of investigation success"""
        base_probability = 0.75

        # Factors that influence success
        if len(actions) >= 5:
            base_probability += 0.1  # Comprehensive plan

        high_confidence_actions = [a for a in actions if a.confidence_score >= 0.8]
        if len(high_confidence_actions) >= 3:
            base_probability += 0.1  # High confidence in actions

        if alert_data.get("has_clear_evidence"):
            base_probability += 0.05  # Strong evidence

        if alert_data.get("has_cooperation"):
            base_probability += 0.05  # Cooperative parties

        return min(base_probability, 0.95)

    def _assess_regulatory_risk(self, alert_data: dict[str, Any]) -> str:
        """Assess regulatory risk level"""
        risk_indicators = []

        if alert_data.get("involves_political_figures"):
            risk_indicators.append("PEP")

        if alert_data.get("cross_border"):
            risk_indicators.append("International Transfer")

        if alert_data.get("high_value_transactions"):
            risk_indicators.append("High Value")

        if len(risk_indicators) >= 2:
            return "high"
        elif len(risk_indicators) >= 1:
            return "medium"
        else:
            return "low"