┌─────────────────────────────────────────────────────────────────────────────┐
│                    REGTECH MATURITY MODEL                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Level 1           Level 2           Level 3           Level 4             │
│  ┌─────────┐      ┌─────────┐      ┌─────────┐      ┌─────────┐          │
│  │ Manual  │      │ Assisted│      │ Automated│     │ Intelligent│        │
│  │ Compliance│ ──→ │ Compliance│ ──→ │ Compliance│ ──→ │ Compliance │      │
│  └─────────┘      └─────────┘      └─────────┘      └─────────┘          │
│                                                                             │
│  Level 5                                                                   │
│  ┌─────────┐                                                             │
│  │ Autonomous│                                                           │
│  │ Compliance│                                                           │
│  └─────────┘                                                             │
│                                                                             │
│  • Spreadsheets   • Basic        • RPA           • AI/ML         • Self-   │
│  • Manual data    • automation   • Data          • Predictive    • healing │
│  • High errors    • Templates    • integration   • Real-time     • Adaptive│
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    NLP FOR REGULATORY CHANGE MANAGEMENT                   │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    REGULATORY TEXT INGESTION                        │   │
│  │  (Regulations, guidelines, announcements)                          │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    TEXT PROCESSING                                  │   │
│  │  (NLP, entity extraction, classification)                          │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    IMPACT ANALYSIS                                  │   │
│  │  (Identify impacted areas, assess risk)                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    ACTION GENERATION                                │   │
│  │  (Generate compliance actions, updates)                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    SELF-SOVEREIGN IDENTITY IN BANKING                     │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CUSTOMER                                          │   │
│  │  (Owns identity data)                                              │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    BLOCKCHAIN                                       │   │
│  │  (DID Registry, Verifiable Credentials)                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    BANK                                             │   │
│  │  (Verifies credentials, provides services)                         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    THIRD PARTIES                                    │   │
│  │  (Regulators, partners, other banks)                               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 6, LESSON 6: FUTURE OF REGTECH
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import hashlib
import json
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("FUTURE OF REGTECH – AI, BLOCKCHAIN, AND REGULATORY SANDBOXES")
print("="*70)

# ----------------------------------------------------------------
# PART A: REGTECH MATURITY ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: RegTech Maturity Assessment")
print("-"*60)

maturity_dimensions = {
    'RegTech Adoption': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'AI/ML Compliance': {'Current Score': 2, 'Target Score': 5, 'Priority': 'High'},
    'Blockchain for Compliance': {'Current Score': 1, 'Target Score': 4, 'Priority': 'Medium'},
    'Regulatory Sandbox Engagement': {'Current Score': 2, 'Target Score': 4, 'Priority': 'Medium'},
    'Predictive Compliance': {'Current Score': 1, 'Target Score': 4, 'Priority': 'High'},
    'Real-Time Reporting': {'Current Score': 2, 'Target Score': 5, 'Priority': 'High'},
    'Automation Rate': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Compliance-as-a-Service': {'Current Score': 2, 'Target Score': 4, 'Priority': 'Medium'}
}

maturity_df = pd.DataFrame(maturity_dimensions).T
print("RegTech Maturity Assessment:")
print(maturity_df)

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
dimensions = list(maturity_df.index)
current = maturity_df['Current Score'].tolist()
target = maturity_df['Target Score'].tolist()

x = np.arange(len(dimensions))
width = 0.35

ax.barh(x - width/2, current, width, label='Current', color='blue', alpha=0.7)
ax.barh(x + width/2, target, width, label='Target', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(dimensions)
ax.set_xlabel('Maturity Score (1-5)')
ax.set_title('RegTech Maturity Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('regtech_maturity.png', dpi=300, bbox_inches='tight')
plt.show()
print("RegTech maturity visualisation saved as 'regtech_maturity.png'")

# ----------------------------------------------------------------
# PART B: NLP FOR REGULATORY CHANGE MANAGEMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: NLP for Regulatory Change Management")
print("-"*60)

class RegulatoryNLP:
    """Simulate NLP for regulatory change management."""
    
    def __init__(self):
        self.regulations = []
        self.changes = []
    
    def ingest_regulation(self, reg_id, text, effective_date):
        """Ingest a regulatory document."""
        reg = {
            'id': reg_id,
            'text': text,
            'effective_date': effective_date,
            'ingested_at': datetime.now().isoformat()
        }
        self.regulations.append(reg)
        return reg
    
    def detect_changes(self, new_text):
        """Detect changes in regulatory text."""
        # Simulate NLP change detection
        changes = []
        
        # Simulate change detection
        if 'GDPR' in new_text:
            changes.append({
                'type': 'Data Privacy',
                'impact': 'High',
                'description': 'GDPR Article 30 updates'
            })
        
        if 'AML' in new_text or 'KYC' in new_text:
            changes.append({
                'type': 'AML/KYC',
                'impact': 'Medium',
                'description': 'Enhanced due diligence requirements'
            })
        
        if 'Basel' in new_text:
            changes.append({
                'type': 'Capital',
                'impact': 'High',
                'description': 'Basel III capital requirements'
            })
        
        if 'ESG' in new_text:
            changes.append({
                'type': 'Sustainability',
                'impact': 'Medium',
                'description': 'ESG reporting requirements'
            })
        
        self.changes.extend(changes)
        return changes
    
    def generate_actions(self, changes):
        """Generate compliance actions from changes."""
        actions = []
        for change in changes:
            actions.append({
                'change': change['description'],
                'action': f'Update {change["type"]} compliance processes',
                'priority': change['impact']
            })
        return actions

# Test regulatory NLP
reg_nlp = RegulatoryNLP()

# Sample regulatory text
reg_text = """
The new regulation updates GDPR requirements for data processing.
AML/KYC procedures must include enhanced due diligence for high-risk customers.
Basel III capital requirements are updated for climate risk.
ESG reporting will be mandatory from 2025.
"""

# Ingest regulation
reg_nlp.ingest_regulation('REG-2024-001', reg_text, '2024-12-31')

# Detect changes
changes = reg_nlp.detect_changes(reg_text)
print("Detected Regulatory Changes:")
for change in changes:
    print(f"  {change['type']}: {change['description']} (Impact: {change['impact']})")

# Generate actions
actions = reg_nlp.generate_actions(changes)
print("\nGenerated Compliance Actions:")
for action in actions:
    print(f"  {action['action']} (Priority: {action['priority']})")

# ----------------------------------------------------------------
# PART C: BLOCKCHAIN FOR REGULATORY REPORTING
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Blockchain for Regulatory Reporting")
print("-"*60)

class BlockchainRegulatoryReport:
    """Simulate blockchain-based regulatory reporting."""
    
    def __init__(self):
        self.chain = []
        self.blocks = []
    
    def create_block(self, data):
        """Create a block for regulatory reporting."""
        block = {
            'index': len(self.blocks) + 1,
            'timestamp': datetime.now().isoformat(),
            'data': data,
            'previous_hash': self._get_previous_hash(),
            'hash': self._calculate_hash(data)
        }
        self.blocks.append(block)
        return block
    
    def _get_previous_hash(self):
        """Get the hash of the previous block."""
        if self.blocks:
            return self.blocks[-1]['hash']
        return '0'
    
    def _calculate_hash(self, data):
        """Calculate the hash of a block."""
        block_string = json.dumps(data, sort_keys=True)
        return hashlib.sha256(block_string.encode()).hexdigest()
    
    def report_compliance(self, report_type, data):
        """Report compliance using blockchain."""
        report = {
            'report_type': report_type,
            'data': data,
            'reported_at': datetime.now().isoformat()
        }
        block = self.create_block(report)
        return block
    
    def get_audit_trail(self):
        """Get the full audit trail."""
        return self.blocks

# Test blockchain reporting
blockchain_report = BlockchainRegulatoryReport()

# Report compliance
report1 = blockchain_report.report_compliance('AML', {
    'suspicious_transactions': 5,
    'status': 'Reviewed',
    'officer': 'Compliance Team'
})

report2 = blockchain_report.report_compliance('Basel III', {
    'cet1_ratio': 11.5,
    'tier1_ratio': 13.2,
    'total_capital_ratio': 15.8
})

report3 = blockchain_report.report_compliance('GDPR', {
    'data_requests': 12,
    'fulfilled': 10,
    'pending': 2
})

print("Blockchain Regulatory Reporting:")
print(f"Total blocks: {len(blockchain_report.blocks)}")
print("\nAudit Trail:")
for block in blockchain_report.get_audit_trail():
    print(f"  Block {block['index']}: {block['data']['report_type']} - {block['timestamp']}")

# ----------------------------------------------------------------
# PART D: REGULATORY SANDBOX SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Regulatory Sandbox Simulation")
print("-"*60)

class RegulatorySandbox:
    """Simulate a regulatory sandbox environment."""
    
    def __init__(self, name, regulator):
        self.name = name
        self.regulator = regulator
        self.participants = []
        self.projects = []
        self.approvals = []
    
    def add_participant(self, participant_name, project_description):
        """Add a participant to the sandbox."""
        participant = {
            'name': participant_name,
            'project': project_description,
            'status': 'Application Received',
            'submitted_at': datetime.now().isoformat()
        }
        self.participants.append(participant)
        return participant
    
    def review_application(self, participant_name):
        """Review a sandbox application."""
        participant = next((p for p in self.participants if p['name'] == participant_name), None)
        if not participant:
            return {'error': 'Participant not found'}
        
        # Simulate review
        is_approved = np.random.random() > 0.1
        participant['status'] = 'Approved' if is_approved else 'Rejected'
        participant['reviewed_at'] = datetime.now().isoformat()
        
        if is_approved:
            self.approvals.append({
                'participant': participant_name,
                'approved_at': datetime.now().isoformat(),
                'duration': '12 months'
            })
        
        return participant
    
    def get_sandbox_status(self):
        """Get sandbox status."""
        return {
            'total_participants': len(self.participants),
            'approved': len([p for p in self.participants if p['status'] == 'Approved']),
            'rejected': len([p for p in self.participants if p['status'] == 'Rejected']),
            'pending': len([p for p in self.participants if p['status'] == 'Application Received'])
        }

# Create sandbox
sandbox = RegulatorySandbox('FinTech Innovation Sandbox', 'FCA')

# Add participants
sandbox.add_participant('Digital Bank', 'AI-powered lending platform')
sandbox.add_participant('FinTech X', 'Blockchain-based KYC')
sandbox.add_participant('InsureTech Y', 'AI-driven insurance pricing')
sandbox.add_participant('WealthTech Z', 'Robo-advisory with generative AI')

print("Regulatory Sandbox Simulation:")
print(f"Sandbox: {sandbox.name} (Regulator: {sandbox.regulator})")

# Review applications
for participant in sandbox.participants:
    result = sandbox.review_application(participant['name'])
    print(f"  {participant['name']}: {result['status']}")

# Get status
status = sandbox.get_sandbox_status()
print(f"\nSandbox Status:")
print(f"  Total Participants: {status['total_participants']}")
print(f"  Approved: {status['approved']}")
print(f"  Rejected: {status['rejected']}")
print(f"  Pending: {status['pending']}")

# ----------------------------------------------------------------
# PART E: REGTECH METRICS DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: RegTech Metrics Dashboard")
print("-"*60)

regtech_metrics = pd.DataFrame({
    'Metric': [
        'RegTech Adoption Rate',
        'AI Compliance Coverage',
        'Blockchain Compliance Usage',
        'Regulatory Sandbox Participation',
        'Predictive Compliance Accuracy',
        'Real-Time Reporting Rate',
        'Automation Rate',
        'Compliance Cost Reduction'
    ],
    'Current Value': [
        '45%',
        '30%',
        '10%',
        '2 projects',
        '65%',
        '35%',
        '55%',
        '20%'
    ],
    'Target Value': [
        '> 80%',
        '> 80%',
        '> 50%',
        '5+ projects',
        '> 90%',
        '> 80%',
        '> 90%',
        '> 50%'
    ],
    'Status': ['🔴', '🔴', '🔴', '🟡', '🟡', '🔴', '🟡', '🟡']
})

print("RegTech Metrics Dashboard:")
print(regtech_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART F: REGTECH ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: RegTech Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Establish RegTech foundation.",
        "Activities": [
            "Implement AI-powered compliance monitoring.",
            "Adopt regulatory sandbox participation.",
            "Establish regulatory change management.",
            "Implement basic automation."
        ],
        "Success Metrics": ["RegTech adoption > 60%", "Automation rate > 70%"]
    },
    "Phase 2 (6-12 months) – Scale": {
        "Focus": "Scale RegTech capabilities.",
        "Activities": [
            "Implement predictive compliance.",
            "Adopt blockchain for regulatory reporting.",
            "Scale automation across functions.",
            "Engage with regulatory sandboxes."
        ],
        "Success Metrics": ["RegTech adoption > 75%", "Predictive compliance accuracy > 80%"]
    },
    "Phase 3 (12-24 months) – Advanced": {
        "Focus": "Advanced RegTech capabilities.",
        "Activities": [
            "Implement AI-driven regulatory change management.",
            "Deploy blockchain-based audit trails.",
            "Achieve real-time reporting.",
            "Build RegTech ecosystem."
        ],
        "Success Metrics": ["RegTech adoption > 90%", "Real-time reporting > 80%"]
    },
    "Phase 4 (24+ months) – Leadership": {
        "Focus": "Industry-leading RegTech.",
        "Activities": [
            "Implement autonomous compliance.",
            "Build predictive regulatory intelligence.",
            "Achieve industry leadership.",
            "Establish RegTech culture."
        ],
        "Success Metrics": ["Industry-leading RegTech", "Continuous improvement"]
    }
}

for phase, details in roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Summary and Recommendations")
print("="*70)

print("""
Future of RegTech – Key Takeaways:

1. RegTech is evolving from manual to autonomous compliance.
2. AI-powered compliance: predictive compliance, intelligent monitoring, automated reporting.
3. Blockchain for compliance: immutable audit trails, SSI, smart contracts.
4. Regulatory sandboxes: controlled environments for innovation.
5. Key metrics: RegTech adoption, AI coverage, predictive accuracy, cost reduction.
6. Roadmap: foundation → scale → advanced → leadership.

Recommendations:
  - Implement AI-powered compliance monitoring.
  - Engage with regulatory sandboxes.
  - Adopt blockchain for regulatory reporting.
  - Build predictive compliance capabilities.
  - Scale automation across functions.
  - Foster innovation through RegTech.
""")

print("="*70)
print("END OF LESSON 6 – MODULE 6")
print("="*70)