┌─────────────────────────────────────────────────────────────────────────────┐
│                    REGULATORY COMPLIANCE FRAMEWORK                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    REGULATORY IDENTIFICATION                        │   │
│  │  Identify applicable regulations, monitor changes                  │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    RISK ASSESSMENT                                  │   │
│  │  Assess compliance risks, impact assessment                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CONTROLS IMPLEMENTATION                          │   │
│  │  Implement compliance controls, policies, procedures               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    MONITORING & TESTING                             │   │
│  │  Monitor compliance, test controls, conduct audits                 │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    REPORTING & REMEDIATION                         │   │
│  │  Report to regulators, remediate issues                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 6, LESSON 1: THE REGULATORY LANDSCAPE
# ===================================================================

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

print("="*70)
print("THE REGULATORY LANDSCAPE IN DIGITAL BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: REGULATORY INVENTORY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Regulatory Inventory")
print("-"*60)

# Define regulatory inventory
regulations = pd.DataFrame({
    'Regulation': ['GDPR', 'PSD2', 'AML/KYC', 'Basel III', 'IFRS 9', 'ECOA', 'CCPA', 'FATF', 'MiCA', 'EU AI Act'],
    'Region': ['EU', 'EU', 'Global', 'Global', 'Global/US', 'US', 'US', 'Global', 'EU', 'EU'],
    'Status': ['Active', 'Active', 'Active', 'Active', 'Active', 'Active', 'Active', 'Active', 'Active', 'Pending'],
    'Compliance Deadline': ['2018-05-25', '2019-09-14', 'Ongoing', 'Ongoing', '2018-01-01', 'Ongoing', '2020-01-01', 'Ongoing', '2024-12-30', '2026-01-01'],
    'Compliance Status': ['✅', '✅', '🟡', '🟡', '✅', '🟡', '✅', '🟡', '🟡', '🟡'],
    'Impact': ['High', 'High', 'High', 'High', 'High', 'Medium', 'High', 'High', 'Medium', 'High']
})

print("Regulatory Inventory:")
print(regulations.to_string(index=False))

# ----------------------------------------------------------------
# PART B: REGULATORY MATURITY ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Regulatory Maturity Assessment")
print("-"*60)

maturity_dimensions = {
    'Regulatory Identification': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Risk Assessment': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Controls Implementation': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Monitoring & Testing': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Reporting & Remediation': {'Current Score': 3, 'Target Score': 4, 'Priority': 'Medium'},
    'RegTech Adoption': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Data Privacy': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'AML Compliance': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

maturity_df = pd.DataFrame(maturity_dimensions).T
print("Regulatory 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('Regulatory Maturity Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

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

# ----------------------------------------------------------------
# PART C: REGULATORY RISK ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Regulatory Risk Assessment")
print("-"*60)

# Define regulatory risks
regulatory_risks = pd.DataFrame({
    'Risk': [
        'Non-Compliance Penalties',
        'Data Breach Fines',
        'Reputational Damage',
        'Operational Disruption',
        'Customer Complaints',
        'Regulatory Scrutiny',
        'Third-Party Risk',
        'Technology Risk'
    ],
    'Likelihood (1-5)': [3, 3, 4, 3, 4, 4, 3, 3],
    'Impact (1-5)': [5, 5, 5, 4, 3, 5, 4, 4],
    'Risk Score': [15, 15, 20, 12, 12, 20, 12, 12],
    'Mitigation': [
        'Compliance program, monitoring',
        'Data protection controls, encryption',
        'Transparency, communication',
        'BCP, DR planning',
        'Customer service, complaint handling',
        'Regulatory engagement, compliance',
        'Third-party management',
        'Technology governance, controls'
    ]
})

print("Regulatory Risk Assessment:")
print(regulatory_risks.to_string(index=False))

# ----------------------------------------------------------------
# PART D: REGULATORY REPORTING REQUIREMENTS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Regulatory Reporting Requirements")
print("-"*60)

reporting_requirements = pd.DataFrame({
    'Report': [
        'AML/SAR Reports',
        'Basel III Reports',
        'IFRS 9 Reports',
        'GDPR Breach Reports',
        'PSD2 Compliance Reports',
        'Fair Lending Reports'
    ],
    'Frequency': ['Monthly', 'Quarterly', 'Quarterly', '72 hours', 'Annual', 'Annual'],
    'Owner': ['Compliance', 'Risk', 'Finance', 'Privacy', 'Compliance', 'Compliance'],
    'Status': ['Automated', 'Partial', 'Partial', 'Manual', 'Manual', 'Manual'],
    'Priority': ['High', 'High', 'High', 'High', 'Medium', 'Medium']
})

print("Regulatory Reporting Requirements:")
print(reporting_requirements.to_string(index=False))

# ----------------------------------------------------------------
# PART E: REGTECH USE CASES
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: RegTech Use Cases")
print("-"*60)

regtech_use_cases = pd.DataFrame({
    'Use Case': [
        'KYC Automation',
        'AML Transaction Monitoring',
        'Regulatory Reporting',
        'Compliance Monitoring',
        'Data Privacy Management',
        'Risk Assessment',
        'Fraud Detection',
        'Sanctions Screening'
    ],
    'Technology': [
        'AI/ML, Biometrics',
        'AI/ML, Rule Engines',
        'Automation, AI',
        'AI, RPA',
        'DLP, Encryption',
        'AI/ML',
        'AI/ML',
        'AI/ML'
    ],
    'Benefit': [
        'Faster onboarding',
        'Real-time monitoring',
        'Reduced errors',
        'Continuous compliance',
        'Data protection',
        'Better risk management',
        'Reduced fraud',
        'Compliance'
    ],
    'Status': ['✅', '✅', '🟡', '🟡', '✅', '🟡', '✅', '🟡']
})

print("RegTech Use Cases:")
print(regtech_use_cases.to_string(index=False))

# ----------------------------------------------------------------
# PART F: REGULATORY COMPLIANCE METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Regulatory Compliance Metrics")
print("-"*60)

compliance_metrics = pd.DataFrame({
    'Metric': [
        'Compliance Rate',
        'Regulatory Incident Rate',
        'Audit Findings',
        'KYC Completion Rate',
        'AML Alert Closure Rate',
        'Data Breach Incidents',
        'Regulatory Reporting Accuracy',
        'Customer Complaint Rate'
    ],
    'Current Value': [
        '82%',
        '4/month',
        '12/year',
        '78%',
        '65%',
        '2/month',
        '88%',
        '3.5%'
    ],
    'Target Value': [
        '> 95%',
        '0/month',
        '< 5/year',
        '> 95%',
        '> 90%',
        '0/month',
        '> 98%',
        '< 1%'
    ],
    'Status': ['🟡', '🟡', '🟡', '🔴', '🔴', '🟡', '🟡', '🔴']
})

print("Regulatory Compliance Metrics:")
print(compliance_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART G: REGULATORY ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART G: Regulatory Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Establish regulatory compliance foundation.",
        "Activities": [
            "Complete regulatory inventory and gap assessment.",
            "Implement compliance policies and procedures.",
            "Establish compliance monitoring and reporting.",
            "Implement KYC/AML automation."
        ],
        "Success Metrics": ["Compliance rate > 85%", "KYC completion > 85%"]
    },
    "Phase 2 (6-12 months) – Scale": {
        "Focus": "Scale compliance capabilities.",
        "Activities": [
            "Implement RegTech solutions.",
            "Automate regulatory reporting.",
            "Implement continuous monitoring.",
            "Enhance data protection controls."
        ],
        "Success Metrics": ["Compliance rate > 90%", "RegTech adoption > 70%"]
    },
    "Phase 3 (12-24 months) – Advanced": {
        "Focus": "Advanced compliance capabilities.",
        "Activities": [
            "Implement AI-powered compliance monitoring.",
            "Deploy predictive compliance analytics.",
            "Build compliance dashboards.",
            "Establish compliance culture."
        ],
        "Success Metrics": ["Compliance rate > 95%", "Regulatory incidents = 0"]
    },
    "Phase 4 (24+ months) – Leadership": {
        "Focus": "Industry-leading compliance.",
        "Activities": [
            "Implement autonomous compliance.",
            "Build predictive regulatory intelligence.",
            "Achieve industry leadership.",
            "Establish compliance as a competitive advantage."
        ],
        "Success Metrics": ["Industry-leading compliance", "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 H: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
Regulatory Landscape – Key Takeaways:

1. Key regulations: GDPR, PSD2, AML/KYC, Basel III, IFRS 9, ECOA, CCPA, FATF, MiCA, EU AI Act.
2. Compliance framework: identification → risk assessment → controls → monitoring → reporting.
3. RegTech automates and enhances compliance processes.
4. Key metrics: compliance rate, regulatory incidents, audit findings, KYC completion.
5. Roadmap: foundation → scale → advanced → leadership.

Recommendations:
  - Complete a regulatory inventory and gap assessment.
  - Implement compliance policies and controls.
  - Adopt RegTech for automation.
  - Monitor and report compliance metrics.
  - Engage with regulators proactively.
  - Build a compliance culture.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 6")
print("="*70)