Objective: Build an anomaly detection model to identify suspicious Mobile Money transactions, using MTN Ghana’s simulated transaction data.

Detailed Explanation:

  • Dataset: Simulated MTN Mobile Money transaction data (e.g., transaction amount, time, sender/receiver ID, location).
    • Example: A CSV file with columns: transaction_id, amount, time, sender_id, receiver_id, location.
    • MTN Context: Reflects real-world Mobile Money patterns, anonymized for privacy.
  • Approach: Use anomaly detection with isolation forests, a machine learning algorithm that identifies outliers by isolating data points.
    • How It Works: Isolation forests create random splits to isolate anomalies faster than normal data, ideal for detecting fraudulent transactions.
    • Example: Flagging a transaction of GHS 10,000 at 2 a.m. as suspicious due to its deviation from typical patterns.
  • Tools: Python, pandas (data processing), scikit-learn (isolation forests).
  • Steps:
    1. Data Preprocessing: Normalize numerical features (e.g., transaction amounts) and encode categorical features (e.g., location).
    2. Model Training: Train an isolation forest to detect anomalies.
    3. Evaluation: Identify transactions flagged as anomalies and validate against known fraud cases.
  • Example Code: python
     

    import pandas as pd
    from sklearn.ensemble import IsolationForest
     
    # Load dataset
    data = pd.read_csv(‘mtn_transaction_data.csv’)
    X = data[[‘amount’, ‘time’, ‘location_encoded’]]
     
    # Train isolation forest
    model = IsolationForest(contamination=0.01, random_state=42)
    model.fit(X)
     
    # Predict anomalies
    predictions = model.predict(X) # -1 for anomalies, 1 for normal
    data[‘anomaly’] = predictions
    anomalies = data[data[‘anomaly’] == -1]
    print(f”Detected {len(anomalies)} suspicious transactions”)
     
  • MTN-Specific Example: MTN Ghana uses isolation forests to detect suspicious Mobile Money transfers (e.g., rapid, high-value transactions), preventing fraud and enhancing user trust.
  • Benefits:
    • Protects customers from financial losses.
    • Strengthens MTN’s Mobile Money reputation.
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