┌─────────────────────────────────────────────────────────────────────────────┐
│                    DIGITAL BANKING OPERATIONAL VALUE CHAIN                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CUSTOMER ACQUISITION                             │   │
│  │  (Onboarding, KYC, Account Opening)                                │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CUSTOMER SERVICING                               │   │
│  │  (Support, Queries, Complaints, Chatbots)                          │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    TRANSACTION PROCESSING                           │   │
│  │  (Payments, Transfers, Clearing, Settlement)                       │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    BACK-OFFICE PROCESSING                           │   │
│  │  (Document Processing, Reconciliation, Reporting)                  │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    RISK & COMPLIANCE                                │   │
│  │  (Fraud Detection, AML, Regulatory Reporting)                      │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    FINANCE & ACCOUNTING                             │   │
│  │  (General Ledger, Reconciliation, Financial Reporting)             │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    DIGITAL ACCOUNT OPENING WORKFLOW                       │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Step 1                Step 2                Step 3                       │
│  ┌─────────────┐      ┌─────────────┐      ┌─────────────┐               │
│  │ Application │ ──→  │ Identity    │ ──→  │ Verification│               │
│  │ (Customer  │      │ Verification│      │ (Automated  │               │
│  │  provides  │      │ (Document & │      │  checks)    │               │
│  │  details)  │      │  Biometric) │      │             │               │
│  └─────────────┘      └─────────────┘      └─────────────┘               │
│                           │                     │                         │
│                           v                     v                         │
│  Step 4                Step 5                Step 6                       │
│  ┌─────────────┐      ┌─────────────┐      ┌─────────────┐               │
│  │ Approval    │ ──→  │ Account     │ ──→  │ Welcome &   │               │
│  │ (Conditional│      │ Setup       │      │ Activation  │               │
│  │  approval)  │      │ (Account    │      │ (Customer   │               │
│  │             │      │  creation)  │      │  notified)  │               │
│  └─────────────┘      └─────────────┘      └─────────────┘               │
│                                                                             │
│  Total Time: 5-10 minutes (Digital) vs 2-3 days (Traditional)             │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 3, LESSON 1: DIGITAL BANKING OPERATIONS OVERVIEW
# ===================================================================

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("DIGITAL BANKING OPERATIONS – OVERVIEW")
print("="*70)

# ----------------------------------------------------------------
# PART A: OPERATIONAL METRICS DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Operational Metrics Dashboard")
print("-"*60)

# Define operational metrics
metrics = {
    'Metric': [
        'STP Rate',
        'Operational Cost per Transaction',
        'Average Processing Time',
        'Error Rate',
        'SLA Adherence',
        'Customer Service Resolution Time',
        'First Contact Resolution',
        'Compliance Adherence'
    ],
    'Current Value': [
        '82%',
        '$0.45',
        '4.2 min',
        '0.8%',
        '97%',
        '3.5 hours',
        '76%',
        '95%'
    ],
    'Target Value': [
        '> 90%',
        '< $0.30',
        '< 2 min',
        '< 0.5%',
        '> 99%',
        '< 2 hours',
        '> 80%',
        '100%'
    ],
    'Status': ['🟡', '🟡', '🔴', '🟡', '🟡', '🔴', '🟡', '🟡']
}

metrics_df = pd.DataFrame(metrics)
print("Operational Metrics Dashboard:")
print(metrics_df.to_string(index=False))

# ----------------------------------------------------------------
# PART B: PROCESS EFFICIENCY ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Process Efficiency Analysis")
print("-"*60)

# Simulate process efficiency data
processes = ['Account Opening', 'Transaction Processing', 'Customer Service', 
             'Document Processing', 'Reconciliation', 'Regulatory Reporting']
manual_time = [120, 15, 45, 60, 90, 180]  # Minutes
digital_time = [8, 1, 5, 3, 10, 15]        # Minutes
manual_cost = [25.00, 3.50, 12.00, 15.00, 22.00, 45.00]
digital_cost = [2.50, 0.45, 1.50, 0.80, 2.50, 5.00]

efficiency_df = pd.DataFrame({
    'Process': processes,
    'Manual Time (min)': manual_time,
    'Digital Time (min)': digital_time,
    'Time Reduction (%)': [(m - d) / m * 100 for m, d in zip(manual_time, digital_time)],
    'Manual Cost ($)': manual_cost,
    'Digital Cost ($)': digital_cost,
    'Cost Reduction (%)': [(m - d) / m * 100 for m, d in zip(manual_cost, digital_cost)]
})

print("Process Efficiency Analysis:")
print(efficiency_df.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Time Reduction
ax = axes[0]
x = np.arange(len(processes))
width = 0.35
ax.barh(x - width/2, manual_time, width, label='Manual', color='red', alpha=0.7)
ax.barh(x + width/2, digital_time, width, label='Digital', color='green', alpha=0.7)
ax.set_yticks(x)
ax.set_yticklabels(processes)
ax.set_xlabel('Time (minutes)')
ax.set_title('Manual vs Digital Processing Time')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

# Cost Reduction
ax = axes[1]
cost_reduction = [(m - d) / m * 100 for m, d in zip(manual_cost, digital_cost)]
bars = ax.barh(processes, cost_reduction, color='teal', alpha=0.7)
ax.set_xlabel('Cost Reduction (%)')
ax.set_title('Cost Reduction by Process')
for bar, reduction in zip(bars, cost_reduction):
    ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, 
            f'{reduction:.0f}%', ha='left', va='center')
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('process_efficiency.png', dpi=300, bbox_inches='tight')
plt.show()
print("Process efficiency visualisation saved as 'process_efficiency.png'")

# ----------------------------------------------------------------
# PART C: OPERATIONAL TRANSFORMATION ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Operational Transformation Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Digitisation": {
        "Focus": "Replace paper-based processes with digital alternatives.",
        "Activities": [
            "Implement digital forms and e-signatures.",
            "Digitise document management.",
            "Enable online application and approvals."
        ],
        "Success Metrics": ["Paper usage reduced by 50%", "Processing time reduced by 30%"]
    },
    "Phase 2 (6-12 months) – Automation": {
        "Focus": "Automate repetitive tasks using RPA and workflow tools.",
        "Activities": [
            "Implement RPA for data entry and reconciliation.",
            "Automate report generation and distribution.",
            "Implement workflow automation for approvals."
        ],
        "Success Metrics": ["STP rate > 80%", "Manual effort reduced by 60%"]
    },
    "Phase 3 (12-24 months) – Intelligent Automation": {
        "Focus": "Combine RPA with AI for end-to-end automation.",
        "Activities": [
            "Implement AI-powered document processing (OCR + NLP).",
            "Implement predictive analytics for exceptions.",
            "Enable intelligent decision-making."
        ],
        "Success Metrics": ["STP rate > 90%", "Error rate < 0.5%"]
    },
    "Phase 4 (24+ months) – Autonomous Operations": {
        "Focus": "Self-optimising, self-healing operations.",
        "Activities": [
            "Implement cognitive automation.",
            "Enable self-service analytics and reporting.",
            "Develop predictive and prescriptive analytics."
        ],
        "Success Metrics": ["STP rate > 95%", "Zero-touch operations"]
    }
}

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 D: OPERATIONAL TECHNOLOGY STACK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Operational Technology Stack")
print("-"*60)

tech_stack = {
    "Core Banking System": {
        "Technologies": ["Finacle", "Temenos", "Oracle FLEXCUBE", "Mambu"],
        "Capabilities": ["Core banking", "Transaction processing", "Account management"]
    },
    "Digital Onboarding": {
        "Technologies": ["Onfido", "Jumio", "Trulioo"],
        "Capabilities": ["eKYC", "Document verification", "Biometrics"]
    },
    "RPA": {
        "Technologies": ["UiPath", "Automation Anywhere", "Blue Prism"],
        "Capabilities": ["Task automation", "Data entry", "Reconciliation"]
    },
    "Workflow Management": {
        "Technologies": ["Pega", "Appian", "ServiceNow"],
        "Capabilities": ["Process orchestration", "Approval workflows", "Case management"]
    },
    "Document Management": {
        "Technologies": ["SharePoint", "OpenText", "Adobe Document Cloud"],
        "Capabilities": ["Document storage", "Version control", "E-signatures"]
    },
    "AI/ML": {
        "Technologies": ["TensorFlow", "PyTorch", "AWS SageMaker"],
        "Capabilities": ["Fraud detection", "Document processing", "Predictive analytics"]
    },
    "Analytics": {
        "Technologies": ["Power BI", "Tableau", "Looker"],
        "Capabilities": ["Reporting", "Dashboards", "Data visualisation"]
    }
}

print("Operational Technology Stack:")
for layer, details in tech_stack.items():
    print(f"\n{layer}:")
    print(f"  Technologies: {', '.join(details['Technologies'])}")
    print(f"  Capabilities: {', '.join(details['Capabilities'])}")

# ----------------------------------------------------------------
# PART E: OPERATIONAL RISK MANAGEMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Operational Risk Management")
print("-"*60)

risks = pd.DataFrame({
    'Risk': [
        'System Failure',
        'Data Breach',
        'Process Error',
        'Fraud',
        'Regulatory Non-Compliance',
        'Third-Party Failure',
        'Human Error'
    ],
    'Likelihood': ['Medium', 'Low', 'Medium', 'Medium', 'Medium', 'Low', 'High'],
    'Impact': ['High', 'Critical', 'Medium', 'High', 'Critical', 'High', 'Medium'],
    'Mitigation': [
        'DR/BCP, system redundancy',
        'Security protocols, encryption',
        'Automated controls, validation',
        'Real-time monitoring, AI detection',
        'Compliance monitoring, audits',
        'Vendor due diligence, SLAs',
        'Automation, training, procedures'
    ]
})

print("Operational Risk Management:")
print(risks.to_string(index=False))

# ----------------------------------------------------------------
# PART F: OPERATIONAL EXCELLENCE FRAMEWORK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Operational Excellence Framework")
print("-"*60)

excellence = {
    "1. Lean Operations": {
        "Principles": [
            "Eliminate waste (non-value-added activities).",
            "Simplify processes (remove unnecessary steps).",
            "Standardise workflows for consistency."
        ],
        "Implementation": "Process mapping, value stream analysis, continuous improvement."
    },
    "2. Automation": {
        "Principles": [
            "Automate repetitive, rule-based tasks.",
            "Use RPA for high-volume, low-complexity work.",
            "Implement AI for intelligent automation."
        ],
        "Implementation": "RPA implementation, workflow automation, AI integration."
    },
    "3. Digitalisation": {
        "Principles": [
            "Replace paper with digital processes.",
            "Enable digital signatures and approvals.",
            "Implement self-service capabilities."
        ],
        "Implementation": "Digital forms, e-signatures, customer portals."
    },
    "4. Data-Driven Operations": {
        "Principles": [
            "Use data to monitor and optimise operations.",
            "Implement real-time dashboards and alerts.",
            "Use predictive analytics for proactive management."
        ],
        "Implementation": "Analytics dashboards, predictive models, real-time monitoring."
    },
    "5. Continuous Improvement": {
        "Principles": [
            "Regularly review and optimise processes.",
            "Collect and act on feedback.",
            "Measure and monitor performance."
        ],
        "Implementation": "Performance metrics, feedback loops, regular reviews."
    }
}

for item, details in excellence.items():
    print(f"\n{item}:")
    for principle in details['Principles']:
        print(f"  • {principle}")
    print(f"  Implementation: {details['Implementation']}")

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

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

print("""
Digital Banking Operations – Key Takeaways:

1. Digital banking operations encompass all back-office and support processes.
2. Key processes: onboarding, transactions, servicing, back-office, risk, finance.
3. Digital operations are faster, cheaper, and more accurate than manual processes.
4. Key KPIs: STP rate, cost per transaction, processing time, error rate.
5. Operational transformation roadmap: digitisation → automation → intelligent automation → autonomous operations.
6. Technology stack: core banking, RPA, workflow, document management, AI, analytics.
7. Operational risks: system failure, data breach, fraud, compliance, human error.

Recommendations:
  - Measure and track operational KPIs.
  - Implement RPA for repetitive tasks.
  - Use AI for intelligent automation.
  - Develop a transformation roadmap.
  - Invest in operational technology.
  - Foster a culture of continuous improvement.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 3")
print("="*70)