┌─────────────────────────────────────────────────────────────────────────────┐
│                    REGULATORY REPORTING AUTOMATION JOURNEY                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐  │
│  │  Manual     │    │  Assisted   │    │  Automated  │    │  Intelligent│  │
│  │  Reporting  │ ──→ │  Reporting  │ ──→ │  Reporting  │ ──→ │  Reporting  │  │
│  └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘  │
│                                                                             │
│  • Spreadsheets   • Templates     • RPA           • AI/ML                  │
│  • Manual data    • Basic         • Data          • Predictive             │
│  • High errors    • automation    • integration    • Real-time             │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 6, LESSON 3: REGULATORY REPORTING AUTOMATION
# ===================================================================

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("REGULATORY REPORTING AUTOMATION")
print("="*70)

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

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

# Define regulatory reports
reports = pd.DataFrame({
    'Report': [
        'Basel III Capital Report',
        'Liquidity Coverage Ratio (LCR)',
        'IFRS 9 ECL Report',
        'AML/SAR Report',
        'Stress Test Report',
        'Financial Statements',
        'Tax Report'
    ],
    'Frequency': ['Quarterly', 'Monthly', 'Quarterly', 'Monthly', 'Annual', 'Quarterly', 'Quarterly'],
    'Regulator': ['Central Bank', 'Central Bank', 'Regulator', 'FIU', 'Central Bank', 'Regulator', 'Tax Authority'],
    'Status': ['Automated', 'Automated', 'Partial', 'Automated', 'Manual', 'Partial', 'Manual'],
    'Automation Priority': ['High', 'High', 'High', 'High', 'Medium', 'Medium', 'Medium']
})

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

# ----------------------------------------------------------------
# PART B: REPORTING AUTOMATION ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Reporting Automation Assessment")
print("-"*60)

automation_dimensions = {
    'Data Collection': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Data Quality': {'Current Score': 2, 'Target Score': 5, 'Priority': 'High'},
    'Report Generation': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Report Validation': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Report Submission': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Workflow Automation': {'Current Score': 2, 'Target Score': 4, 'Priority': 'Medium'},
    'Audit Trail': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

automation_df = pd.DataFrame(automation_dimensions).T
print("Reporting Automation Assessment:")
print(automation_df)

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
dimensions = list(automation_df.index)
current = automation_df['Current Score'].tolist()
target = automation_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('Reporting Automation Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('reporting_automation.png', dpi=300, bbox_inches='tight')
plt.show()
print("Reporting automation visualisation saved as 'reporting_automation.png'")

# ----------------------------------------------------------------
# PART C: BASEL III CAPITAL REPORT SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Basel III Capital Report Simulation")
print("-"*60)

# Generate sample data for Basel III report
np.random.seed(42)
n_banks = 1

# Simulate Basel III data
basel_data = pd.DataFrame({
    'Metric': [
        'Common Equity Tier 1 (CET1)',
        'Additional Tier 1',
        'Tier 1 Capital',
        'Tier 2 Capital',
        'Total Capital',
        'Risk-Weighted Assets',
        'CET1 Ratio',
        'Tier 1 Ratio',
        'Total Capital Ratio',
        'Leverage Ratio',
        'Liquidity Coverage Ratio'
    ],
    'Value': [
        np.random.uniform(8, 12, 1)[0],
        np.random.uniform(1, 2, 1)[0],
        np.random.uniform(10, 14, 1)[0],
        np.random.uniform(1, 2, 1)[0],
        np.random.uniform(12, 15, 1)[0],
        np.random.uniform(80, 120, 1)[0],
        np.random.uniform(8, 12, 1)[0],
        np.random.uniform(10, 14, 1)[0],
        np.random.uniform(12, 15, 1)[0],
        np.random.uniform(3, 5, 1)[0],
        np.random.uniform(100, 120, 1)[0]
    ],
    'Regulatory Minimum': [4.5, 6.0, 8.0, 8.0, 10.5, None, 4.5, 6.0, 8.0, 3.0, 100],
    'Status': ['Pass', 'Pass', 'Pass', 'Pass', 'Pass', None, 'Pass', 'Pass', 'Pass', 'Pass', 'Pass']
})

print("Basel III Capital Report:")
print(basel_data.to_string(index=False))

# ----------------------------------------------------------------
# PART D: REPORT GENERATION WORKFLOW
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Report Generation Workflow")
print("-"*60)

class ReportGenerator:
    """Simulate regulatory report generation."""
    
    def __init__(self):
        self.generation_log = []
        self.reports = []
    
    def collect_data(self, data_sources):
        """Collect data from various sources."""
        self.generation_log.append({
            'step': 'Data Collection',
            'status': 'In Progress',
            'timestamp': datetime.now().isoformat()
        })
        # Simulate data collection
        data = {}
        for source in data_sources:
            data[source] = np.random.random(10).round(2)
        self.generation_log.append({
            'step': 'Data Collection',
            'status': 'Completed',
            'timestamp': datetime.now().isoformat()
        })
        return data
    
    def validate_data(self, data):
        """Validate collected data."""
        self.generation_log.append({
            'step': 'Data Validation',
            'status': 'In Progress',
            'timestamp': datetime.now().isoformat()
        })
        # Simulate validation
        is_valid = np.random.random() > 0.05
        if is_valid:
            self.generation_log.append({
                'step': 'Data Validation',
                'status': 'Completed',
                'timestamp': datetime.now().isoformat()
            })
        else:
            self.generation_log.append({
                'step': 'Data Validation',
                'status': 'Failed',
                'timestamp': datetime.now().isoformat()
            })
        return is_valid
    
    def generate_report(self, data, report_type):
        """Generate a regulatory report."""
        self.generation_log.append({
            'step': 'Report Generation',
            'status': 'In Progress',
            'timestamp': datetime.now().isoformat()
        })
        
        # Simulate report generation
        report = {
            'report_type': report_type,
            'data': data,
            'generated_at': datetime.now().isoformat()
        }
        self.reports.append(report)
        
        self.generation_log.append({
            'step': 'Report Generation',
            'status': 'Completed',
            'timestamp': datetime.now().isoformat()
        })
        return report
    
    def get_workflow_status(self):
        """Get the status of the generation workflow."""
        return pd.DataFrame(self.generation_log)

# Test report generation
generator = ReportGenerator()
data_sources = ['Core Banking', 'Treasury', 'Risk', 'Finance', 'Compliance']

print("Simulating report generation workflow...")
data = generator.collect_data(data_sources)
is_valid = generator.validate_data(data)
if is_valid:
    report = generator.generate_report(data, 'Basel III Capital Report')
    print(f"Report generated: {report['report_type']}")
else:
    print("Report generation failed: Data validation error")

workflow_status = generator.get_workflow_status()
print("\nWorkflow Status:")
print(workflow_status.to_string(index=False))

# ----------------------------------------------------------------
# PART E: REPORTING METRICS DASHBOARD
# ----------------------------------------------------------------

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

reporting_metrics = pd.DataFrame({
    'Metric': [
        'Reporting Accuracy',
        'Reporting Timeliness',
        'Data Quality Score',
        'Report Generation Time',
        'Number of Errors',
        'Compliance Rate',
        'Audit Findings'
    ],
    'Current Value': [
        '92%',
        '88%',
        '78/100',
        '4.2 hours',
        '2.5/report',
        '85%',
        '3'
    ],
    'Target Value': [
        '> 99%',
        '100%',
        '> 95/100',
        '< 2 hours',
        '0',
        '100%',
        '0'
    ],
    'Status': ['🟡', '🟡', '🔴', '🔴', '🟡', '🟡', '🟡']
})

print("Reporting Metrics Dashboard:")
print(reporting_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART F: REGULATORY REPORTING ROADMAP
# ----------------------------------------------------------------

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

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Establish reporting foundation.",
        "Activities": [
            "Implement data collection automation.",
            "Establish data quality controls.",
            "Automate report generation for key reports.",
            "Implement validation and reconciliation."
        ],
        "Success Metrics": ["Reporting accuracy > 95%", "Report generation time < 4 hours"]
    },
    "Phase 2 (6-12 months) – Scale": {
        "Focus": "Scale reporting automation.",
        "Activities": [
            "Automate all regulatory reports.",
            "Implement workflow automation.",
            "Enable self-service reporting.",
            "Establish continuous monitoring."
        ],
        "Success Metrics": ["Reporting accuracy > 98%", "Report generation time < 2 hours"]
    },
    "Phase 3 (12-24 months) – Advanced": {
        "Focus": "Advanced reporting capabilities.",
        "Activities": [
            "Implement AI-powered data validation.",
            "Deploy predictive reporting.",
            "Build real-time reporting dashboards.",
            "Establish reporting analytics."
        ],
        "Success Metrics": ["Reporting accuracy > 99%", "Real-time reporting capability"]
    },
    "Phase 4 (24+ months) – Leadership": {
        "Focus": "Industry-leading reporting.",
        "Activities": [
            "Implement autonomous reporting.",
            "Build AI-driven reporting insights.",
            "Achieve industry leadership.",
            "Establish reporting culture."
        ],
        "Success Metrics": ["Industry-leading reporting", "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: REGULATORY REPORTING CHECKLIST
# ----------------------------------------------------------------

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

reporting_checklist = [
    "✅ All regulatory reports identified.",
    "✅ Report frequency and deadlines documented.",
    "✅ Data sources identified and accessible.",
    "✅ Data quality controls implemented.",
    "✅ Report generation automated.",
    "✅ Report validation and reconciliation in place.",
    "✅ Workflow automation for review and sign-off.",
    "✅ Audit trail maintained.",
    "✅ Report submission automated.",
    "✅ Regulatory reporting metrics tracked."
]

print("Regulatory Reporting Checklist:")
for item in reporting_checklist:
    print(f"  {item}")

# ----------------------------------------------------------------
# PART H: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
Regulatory Reporting Automation – Key Takeaways:

1. Regulatory reporting is critical for compliance in banking.
2. Key reports: Basel III, IFRS 9, AML/SAR, LCR, stress tests, financial statements.
3. Automation journey: manual → assisted → automated → intelligent.
4. Key technologies: RPA, ETL, data quality, workflow, AI/ML, APIs.
5. Data quality dimensions: accuracy, completeness, timeliness, consistency, validity.
6. Key metrics: accuracy, timeliness, data quality, generation time, errors.
7. Roadmap: foundation → scale → advanced → leadership.

Recommendations:
  - Implement automated data collection.
  - Establish data quality controls.
  - Automate report generation and submission.
  - Implement workflow automation.
  - Track reporting metrics.
  - Continuously improve reporting processes.
""")

print("="*70)
print("END OF LESSON 3 – MODULE 6")
print("="*70)