┌─────────────────────────────────────────────────────────────────────────────┐
│                    FUTURE BANKING BUSINESS MODELS                         │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    BANKING-AS-A-SERVICE (BAAS)                      │   │
│  │  Banking infrastructure as a service for non-banks                  │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    EMBEDDED FINANCE                                  │   │
│  │  Financial services integrated into non-financial platforms         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    PLATFORM BANKING                                  │   │
│  │  Bank as a platform for third-party services                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    ECOSYSTEM BANKING                                 │   │
│  │  Bank as part of a broader financial ecosystem                      │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    DATA-DRIVEN BANKING                               │   │
│  │  Data monetisation as a core business model                         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 9, LESSON 4: THE FUTURE OF BANKING BUSINESS MODELS
# ===================================================================

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

print("="*70)
print("THE FUTURE OF BANKING BUSINESS MODELS")
print("="*70)

# ----------------------------------------------------------------
# PART A: BUSINESS MODEL COMPARISON
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Business Model Comparison")
print("-"*60)

business_models = pd.DataFrame({
    'Model': ['Traditional', 'Omnichannel', 'Platform', 'BaaS', 'Embedded Finance', 'Ecosystem'],
    'Revenue Growth': ['Low', 'Medium', 'High', 'Very High', 'Very High', 'High'],
    'Profit Margin': ['Medium', 'Medium', 'High', 'Medium', 'High', 'High'],
    'Customer Engagement': ['Low', 'Medium', 'High', 'High', 'Very High', 'High'],
    'Digital Capability': ['Low', 'Medium', 'High', 'High', 'Very High', 'High'],
    'Innovation': ['Low', 'Medium', 'High', 'High', 'Very High', 'High'],
    'Risk': ['Low', 'Medium', 'Medium', 'High', 'High', 'Medium']
})

print("Business Model Comparison:")
print(business_models.to_string(index=False))

# ----------------------------------------------------------------
# PART B: BUSINESS MODEL MATURITY ASSESSMENT
# -----------------------------------------------------------------

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

maturity_dimensions = {
    'Digital Capability': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Customer Engagement': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Revenue Diversification': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Ecosystem Partnerships': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Innovation Capability': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Data Monetisation': {'Current Score': 2, 'Target Score': 4, 'Priority': 'Medium'},
    'Agility': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

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

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

# ----------------------------------------------------------------
# PART C: REVENUE MODEL ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Revenue Model Analysis")
print("-"*60)

revenue_models = pd.DataFrame({
    'Revenue Model': ['Traditional Banking', 'BaaS', 'Embedded Finance', 'Platform Banking', 'Data Monetisation'],
    'Revenue Source': [
        'Interest, Fees, Commissions',
        'Transaction Fees, Subscription',
        'Transaction Fees, Revenue Share',
        'Referral Fees, Revenue Share',
        'Data Licensing, Insights'
    ],
    'Margin': ['Medium', 'Medium', 'High', 'High', 'Very High'],
    'Scalability': ['Low', 'High', 'Very High', 'High', 'Very High'],
    'Example': [
        'Traditional Banks',
        'Solarisbank',
        'Shopify Capital',
        'Starling Marketplace',
        'Data Analytics'
    ]
})

print("Revenue Model Analysis:")
print(revenue_models.to_string(index=False))

# ----------------------------------------------------------------
# PART D: BUSINESS MODEL SCORECARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Business Model Scorecard")
print("-"*60)

scorecard = pd.DataFrame({
    'Dimension': ['Revenue Diversification', 'Customer Engagement', 'Digital Capability', 
                  'Innovation Pipeline', 'Ecosystem Partnerships', 'Profitability'],
    'Weight (%)': [20, 20, 20, 15, 15, 10],
    'Score (1-10)': [7, 8, 9, 6, 5, 7],
    'Weighted Score': [1.4, 1.6, 1.8, 0.9, 0.75, 0.7]
})

scorecard['Weighted Score'] = scorecard['Weight (%)'] * scorecard['Score (1-10)'] / 100
total_score = scorecard['Weighted Score'].sum()

print("Business Model Scorecard:")
print(scorecard.to_string(index=False))
print(f"\nTotal Score: {total_score:.1f}/10")
print(f"Interpretation: {'Strong' if total_score >= 7 else 'Needs Improvement' if total_score >= 5 else 'Weak'}")

# ----------------------------------------------------------------
# PART E: BUSINESS MODEL ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Business Model Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Build digital foundation.",
        "Activities": [
            "Enhance digital capabilities.",
            "Develop data monetisation strategy.",
            "Build customer engagement capabilities.",
            "Establish innovation pipeline."
        ],
        "Success Metrics": ["Digital penetration > 60%", "Customer engagement > 70%"]
    },
    "Phase 2 (6-12 months) – Scale": {
        "Focus": "Scale new business models.",
        "Activities": [
            "Launch BaaS offerings.",
            "Develop platform capabilities.",
            "Build ecosystem partnerships.",
            "Implement data monetisation."
        ],
        "Success Metrics": ["BaaS revenue > $10M", "Platform integrations > 50"]
    },
    "Phase 3 (12-24 months) – Expansion": {
        "Focus": "Expand business models.",
        "Activities": [
            "Launch embedded finance products.",
            "Build ecosystem banking.",
            "Scale data monetisation.",
            "Achieve industry leadership."
        ],
        "Success Metrics": ["Embedded finance revenue > $25M", "Ecosystem partnerships > 100"]
    },
    "Phase 4 (24+ months) – Leadership": {
        "Focus": "Industry-leading business models.",
        "Activities": [
            "Lead business model innovation.",
            "Build global ecosystem.",
            "Achieve industry leadership.",
            "Continuous improvement."
        ],
        "Success Metrics": ["Industry-leading business models", "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 F: BUSINESS MODEL METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Business Model Metrics Dashboard")
print("-"*60)

business_metrics = pd.DataFrame({
    'Metric': [
        'Revenue Growth',
        'Profit Margin',
        'Customer Lifetime Value (CLV)',
        'Customer Acquisition Cost (CAC)',
        'Net Promoter Score (NPS)',
        'Market Share',
        'Digital Penetration',
        'Innovation Index'
    ],
    'Current Value': [
        '12%',
        '18%',
        '$2,500',
        '$350',
        '55',
        '8%',
        '65%',
        '6/10'
    ],
    'Target Value': [
        '> 15%',
        '> 20%',
        '> $3,500',
        '< $300',
        '> 65',
        '> 12%',
        '> 80%',
        '> 8/10'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡']
})

print("Business Model Metrics Dashboard:")
print(business_metrics.to_string(index=False))

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

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

print("""
Future of Banking Business Models – Key Takeaways:

1. Banking business models are evolving from traditional to ecosystem banking.
2. Future models: BaaS, Embedded Finance, Platform Banking, Ecosystem Banking.
3. BaaS provides banking infrastructure as a service to non-banks.
4. Embedded Finance integrates financial services into non-financial platforms.
5. Platform Banking positions the bank as a marketplace for third-party services.
6. Ecosystem Banking positions the bank as part of a broader financial ecosystem.
7. Data and AI are central to future business models.
8. Key metrics: revenue growth, profit margin, CLV, CAC, NPS.

Recommendations:
  - Build digital and data capabilities.
  - Launch BaaS and platform offerings.
  - Develop embedded finance products.
  - Build ecosystem partnerships.
  - Monetise data and insights.
  - Continuously innovate business models.
""")

print("="*70)
print("END OF LESSON 4 – MODULE 9")
print("="*70)