┌─────────────────────────────────────────────────────────────────────────────┐
│                    FUTURE BANKING LANDSCAPE                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    AI-DRIVEN BANKING                                │   │
│  │  AI-powered personalisation, decision-making, and automation         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    PLATFORM BANKING                                 │   │
│  │  Banks as platforms for third-party services                       │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    EMBEDDED FINANCE                                 │   │
│  │  Financial services integrated into non-financial platforms         │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    SUSTAINABLE FINANCE                              │   │
│  │  ESG-integrated banking, green finance, climate risk               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    AUTONOMOUS BANKING                               │   │
│  │  Self-driving banking, zero-touch operations                       │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 9, LESSON 1: THE FUTURE OF BANKING – A STRATEGIC VISION
# ===================================================================

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 – A STRATEGIC VISION")
print("="*70)

# ----------------------------------------------------------------
# PART A: FUTURE TRENDS ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Future Trends Analysis")
print("-"*60)

trends = pd.DataFrame({
    'Trend': [
        'Generative AI',
        'Quantum Computing',
        'CBDCs',
        'Decentralised Finance',
        'Embedded Finance',
        'ESG Integration',
        'Autonomous Banking'
    ],
    'Impact (1-10)': [10, 8, 7, 8, 9, 8, 7],
    'Timeline (years)': [2, 8, 4, 5, 3, 5, 6],
    'Confidence (1-5)': [5, 3, 4, 4, 5, 5, 4]
})

print("Future Trends Analysis:")
print(trends.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Impact vs Timeline
ax = axes[0]
scatter = ax.scatter(trends['Timeline (years)'], trends['Impact (1-10)'], 
                     s=trends['Confidence (1-5)'] * 100, alpha=0.7)
for i, row in trends.iterrows():
    ax.annotate(row['Trend'], (row['Timeline (years)'] + 0.2, row['Impact (1-10)'] + 0.2))
ax.set_xlabel('Timeline (years)')
ax.set_ylabel('Impact (1-10)')
ax.set_title('Trends: Impact vs Timeline (size = confidence)')
ax.grid(True, alpha=0.3)

# Impact by Trend
ax = axes[1]
trends_sorted = trends.sort_values('Impact (1-10)', ascending=True)
ax.barh(trends_sorted['Trend'], trends_sorted['Impact (1-10)'], color='teal', alpha=0.7)
ax.set_xlabel('Impact (1-10)')
ax.set_title('Trend Impact')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('future_trends_analysis.png', dpi=300, bbox_inches='tight')
plt.show()
print("Future trends visualisation saved as 'future_trends_analysis.png'")

# ----------------------------------------------------------------
# PART B: SCENARIO PLANNING
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Scenario Planning")
print("-"*60)

scenarios = pd.DataFrame({
    'Scenario': [
        'Open Ecosystem',
        'Platform Dominance',
        'Regulatory-Driven',
        'Crypto-Native',
        'Sustainable Banking'
    ],
    'Probability (1-5)': [4, 3, 4, 3, 4],
    'Impact (1-5)': [5, 5, 4, 5, 5],
    'Strategic Response': [
        'Build platform capabilities, API strategy',
        'Differentiate, partner, build ecosystem',
        'Compliance, regulatory engagement',
        'Build crypto capabilities, partnerships',
        'Lead in sustainable finance, ESG integration'
    ]
})

print("Scenario Planning:")
print(scenarios.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
scatter = ax.scatter(scenarios['Probability (1-5)'], scenarios['Impact (1-5)'], 
                     s=200, alpha=0.7)
for i, row in scenarios.iterrows():
    ax.annotate(row['Scenario'], (row['Probability (1-5)'] + 0.1, row['Impact (1-5)'] + 0.1))
ax.set_xlabel('Probability (1-5)')
ax.set_ylabel('Impact (1-5)')
ax.set_title('Scenario Matrix')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('scenario_planning.png', dpi=300, bbox_inches='tight')
plt.show()
print("Scenario planning visualisation saved as 'scenario_planning.png'")

# ----------------------------------------------------------------
# PART C: TECHNOLOGY INVESTMENT PRIORITIES
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Technology Investment Priorities")
print("-"*60)

tech_investment = pd.DataFrame({
    'Technology': ['AI/ML', 'Cloud', 'Blockchain', 'Generative AI', 'Quantum'],
    'Priority': ['High', 'High', 'Medium', 'Medium', 'Low'],
    'Investment ($M)': [50, 30, 15, 20, 5],
    'ROI Potential': ['Very High', 'High', 'Medium', 'High', 'Long-term'],
    'Timeline': ['Now', 'Now', '12 months', '6 months', 'Research']
})

print("Technology Investment Priorities:")
print(tech_investment.to_string(index=False))

# ----------------------------------------------------------------
# PART D: FUTURE READINESS METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Future Readiness Metrics Dashboard")
print("-"*60)

readiness_metrics = pd.DataFrame({
    'Metric': [
        'AI Maturity Score',
        'Cloud Adoption Rate',
        'Digital Penetration',
        'Innovation Pipeline Strength',
        'Partnership Ecosystem',
        'Talent Capability',
        'Regulatory Readiness',
        'Future Vision Clarity'
    ],
    'Current Value': [
        '3.2/5',
        '65%',
        '72%',
        '3.5/5',
        '2.8/5',
        '3.5/5',
        '3.0/5',
        '3.0/5'
    ],
    'Target Value': [
        '> 4.5/5',
        '> 90%',
        '> 85%',
        '> 4.5/5',
        '> 4.0/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡']
})

print("Future Readiness Metrics Dashboard:")
print(readiness_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART E: FUTURE READINESS ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Future Readiness Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-12 months) – Foundation": {
        "Focus": "Build future readiness foundation.",
        "Activities": [
            "Develop AI strategy and capabilities.",
            "Accelerate cloud migration.",
            "Build generative AI capabilities.",
            "Establish strategic foresight team."
        ],
        "Success Metrics": ["AI maturity > 4.0", "Cloud adoption > 80%"]
    },
    "Phase 2 (12-24 months) – Scale": {
        "Focus": "Scale future capabilities.",
        "Activities": [
            "Scale AI across the organisation.",
            "Build platform capabilities.",
            "Develop partnerships for emerging tech.",
            "Implement quantum readiness."
        ],
        "Success Metrics": ["AI maturity > 4.5", "Digital penetration > 85%"]
    },
    "Phase 3 (24-36 months) – Advanced": {
        "Focus": "Advanced future capabilities.",
        "Activities": [
            "Lead in AI-driven banking.",
            "Build autonomous operations.",
            "Achieve regulatory excellence.",
            "Lead in sustainable finance."
        ],
        "Success Metrics": ["Industry-leading AI", "Future-ready organisation"]
    },
    "Phase 4 (36+ months) – Leadership": {
        "Focus": "Industry leadership.",
        "Activities": [
            "Lead industry innovation.",
            "Build global capabilities.",
            "Achieve future-ready leadership.",
            "Continuous improvement."
        ],
        "Success Metrics": ["Industry leadership", "Continuous innovation"]
    }
}

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: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
The Future of Banking – Key Takeaways:

1. Key trends: Generative AI, quantum, CBDCs, DeFi, embedded finance, ESG, autonomous banking.
2. Strategic foresight: scan, analyse, envision, plan, act.
3. Scenarios: open ecosystem, platform dominance, regulatory-driven, crypto-native, sustainable banking.
4. Technology priorities: AI/ML, cloud, blockchain, generative AI.
5. Future readiness metrics: AI maturity, cloud adoption, digital penetration, innovation pipeline.
6. Roadmap: foundation → scale → advanced → leadership.

Recommendations:
  - Develop a strategic foresight capability.
  - Invest in AI and cloud capabilities.
  - Build platform and embedded finance capabilities.
  - Engage with regulators and industry bodies.
  - Prepare for emerging technologies (quantum, blockchain).
  - Lead in sustainable finance.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 9")
print("="*70)