Objective: Collaborate in teams to develop an AI-driven solution for a selected MTN Ghana use case, defining the problem, solution, and implementation plan.

Detailed Explanation:

  • Team Setup: Teams of 3-5 members, formed based on diverse roles (e.g., network engineers, customer service reps).
  • Use Case Options:
    1. Smart Customer Segmentation:
      • Problem: Generic marketing campaigns fail to target specific customer needs.
      • AI Solution: Use k-means clustering to group customers by behavior (e.g., data usage, Mobile Money transactions).
      • MTN Example: Segment Mobile Money users into students, small businesses, and high-value customers for tailored promotions.
      • Implementation: Collect anonymized customer data, apply clustering in Python with scikit-learn, and propose marketing strategies.
    2. AI for 5G Deployment Planning:
      • Problem: Suboptimal tower placement leads to poor 5G coverage.
      • AI Solution: Use regression models to predict signal strength and optimize tower locations.
      • MTN Example: Optimize 5G tower placement in Accra to ensure coverage for business districts.
      • Implementation: Use scikit-learn for regression, leveraging MTN’s signal strength data.
    3. Mobile Money Fraud Detection Models:
      • Problem: Fraudulent transactions threaten Mobile Money trust.
      • AI Solution: Apply isolation forests for anomaly detection in transaction data.
      • MTN Example: Detect suspicious Mobile Money transfers (e.g., large transactions at odd hours).
      • Implementation: Train an isolation forest model in Python, using simulated MTN transaction data.
  • Tasks:
    1. Define Problem: Articulate the challenge (e.g., “High churn among Mobile Money users in rural areas”).
    2. Propose AI Solution: Specify algorithms and tools (e.g., logistic regression in scikit-learn for churn prediction).
    3. Outline Implementation: Detail data sources, model training, and deployment steps (e.g., integrating fraud detection into MTN’s Mobile Money platform).
  • Example Workflow (Fraud Detection):
    • Data: Simulated transaction dataset (transaction_id, amount, time, location).
    • Code: python

      import pandas as pd
      from sklearn.ensemble import IsolationForest
      data = pd.read_csv(‘mtn_transaction_data.csv’)
      X = data[[‘amount’, ‘time’, ‘location_encoded’]]
      model = IsolationForest(contamination=0.01, random_state=42)
      model.fit(X)
      predictions = model.predict(X) # -1 for anomalies
      print(f”Detected {len(data[predictions == -1])} suspicious transactions”)
    • Implementation: Deploy model to flag anomalies in real-time, alerting MTN’s fraud team.
  • Benefits:
    • Hands-on application of AI skills to MTN’s challenges.
    • Encourages teamwork and creative problem-solving.
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