I Built an ML Platform to Monitor Africa's $700B Debt Crisis - Here's What I Learned

Source: Dev.to

The Problem: A $700 Billion Blind Spot

Nine African countries are currently in debt distress. Combined sovereign debt across the continent exceeds $700 billion, with debt service consuming over 40 % of government revenue in several nations.

The 2022 collapse caught many by surprise: Ghana went from “manageable debt levels” to sovereign default in under 18 months. Zambia, Mozambique, and Ethiopia followed similar trajectories.

Core issue: Traditional monitoring relies on lagging indicators. By the time the IMF flags a country as “high risk,” it’s often too late for preventive measures.

I wondered: could machine learning provide earlier warning signals?

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What I Built

Africa‑Debt‑Intelligence is a real‑time sovereign‑debt risk monitoring platform that:

• Aggregates fiscal data from IMF World Economic Outlook and World Bank International Debt Statistics
• Generates risk scores (0‑100 scale) using ML clustering and time‑series analysis
• Forecasts debt trajectories 5 years ahead with confidence intervals
• Provides policy recommendations tailored to each country’s risk profile
• Issues live alerts when fiscal indicators cross critical thresholds

The platform currently monitors 15 Sub‑Saharan African economies, representing 85 % of the region’s GDP.

GitHub Repository:
Tech Stack: Python, React, scikit‑learn, pandas, REST APIs

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Technical Architecture

Data Pipeline

Automated ingestion from public APIs:

def load_and_clean_data(filepath: str) -> pd.DataFrame:
    """
    Load long‑format fiscal data and perform cleaning operations.
    """
    df = pd.read_csv(filepath)

    # Convert time to year format
    df['Year'] = pd.to_datetime(df['Time']).dt.year

    # Handle missing values with forward fill + interpolation
    df = df.groupby(['Country', 'Indicator']).apply(
        lambda x: x.interpolate(method='linear')
    ).reset_index(drop=True)

    # Normalize fiscal indicators to % of GDP
    gdp_data = df[df['Indicator'] == 'GDP'][['Country', 'Year', 'Amount']]
    gdp_data = gdp_data.rename(columns={'Amount': 'GDP'})

    df = df.merge(gdp_data, on=['Country', 'Year'], how='left')

    # Create normalized ratios
    indicators_to_normalize = ['External_Debt', 'Revenue', 'Expenditure', 'Deficit']
    for ind in indicators_to_normalize:
        mask = df['Indicator'] == ind
        df.loc[mask, 'Normalized_Value'] = (
            df.loc[mask, 'Amount'] / df.loc[mask, 'GDP'] * 100
        )

    return df

Key indicators tracked

• Debt‑to‑GDP ratio
• Fiscal balance (% GDP)
• Revenue‑to‑GDP ratio
• Debt service ratio
• GDP growth rate
• Inflation rate
• External debt exposure
• FX reserves (months of imports)

Risk Scoring Model

Combines unsupervised learning with domain‑specific weighting:

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

def generate_risk_scores(df: pd.DataFrame) -> pd.DataFrame:
    """
    Generate composite risk scores using K‑means clustering
    and weighted fiscal indicators.
    """
    features = [
        'Debt_to_GDP', 'Fiscal_Balance', 'Revenue_to_GDP',
        'Debt_Service_Ratio', 'GDP_Growth', 'Inflation'
    ]

    # Standardize features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(df[features])

    # K‑means clustering to identify risk groups
    kmeans = KMeans(n_clusters=4, random_state=42)
    df['Risk_Cluster'] = kmeans.fit_predict(X_scaled)

    # Weighted composite score
    weights = {
        'Debt_to_GDP': 0.25,
        'Debt_Service_Ratio': 0.25,
        'Fiscal_Balance': 0.20,
        'Revenue_to_GDP': 0.15,
        'GDP_Growth': 0.10,
        'Inflation': 0.05
    }

    df['Risk_Score'] = sum(
        df[feature] * weight
        for feature, weight in weights.items()
    )

    # Normalize to 0‑1 scale
    df['Risk_Score'] = (
        (df['Risk_Score'] - df['Risk_Score'].min()) /
        (df['Risk_Score'].max() - df['Risk_Score'].min())
    )

    return df

Risk thresholds

• 0.00‑0.40 – Low Risk (green)
• 0.41‑0.60 – Medium Risk (yellow)
• 0.61‑0.75 – High Risk (orange)
• 0.76‑1.00 – Critical Risk (red)

Time‑Series Forecasting

ARIMA models generate 5‑year debt‑to‑GDP forecasts with confidence intervals:

from statsmodels.tsa.arima.model import ARIMA

def forecast_debt_trajectory(country_data: pd.DataFrame,
                             periods: int = 20) -> dict:
    """
    Generate 5‑year debt‑to‑GDP forecast with confidence intervals.
    """
    model = ARIMA(country_data['Debt_to_GDP'], order=(2, 1, 2))
    fitted_model = model.fit()

    forecast = fitted_model.forecast(steps=periods)
    conf_int = fitted_model.get_forecast(steps=periods).conf_int()

    return {
        'forecast': forecast,
        'lower_bound': conf_int.iloc[:, 0],
        'upper_bound': conf_int.iloc[:, 1]
    }

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The Challenges I Faced

Challenge 1: Data Quality Hell

African macroeconomic data is often revised, irregular, or missing.
Example: Ghana’s debt‑to‑GDP ratio was retroactively revised upward by 15 percentage points in 2023, altering the historical picture.

Solutions

• Cross‑validated against multiple sources (IMF, World Bank, AfDB)
• Interpolated missing quarterly data
• Added data‑quality flags to indicate confidence levels
• Performed manual spot‑checks for outliers

Challenge 2: Defining “Risk”

How to interpret a risk score and validate it?

Solutions

• Back‑tested against historical debt‑distress episodes (2000‑2023)
• Found that scores > 0.70 preceded 8 out of 10 actual crises
• Average lead time: 14 months before distress materialized
• Built a confusion matrix comparing predictions vs. outcomes

Historical validation results

• Ghana 2022: Flagged 18 months early (score 0.82)
• Zambia 2020: Flagged 16 months early (score 0.79)
• Mozambique 2016: Flagged 12 months early (score 0.75)

Challenge 3: Making It Interpretable

Policymakers need to understand why a country is flagged.

Solutions

• Feature‑importance analysis to show drivers of risk scores
• Decomposition of each factor’s contribution
• Policy recommendations tied to specific vulnerabilities
• Natural‑language explanations, e.g., “Risk elevated due to debt service consuming 62 % of revenue”

Challenge 4: Keeping Data Current

APIs may lag or fail, and manual entry isn’t scalable.

Solutions

• Automated ETL pipeline running monthly
• Fallback to cached data when APIs fail
• Data‑freshness indicators displayed on the dashboard