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Python Loan Risk Analysis Using Exploratory Data Analysis

12 min read, 15 sections

Contents
  1. Loading and Initial Data Exploration
  2. Data Preprocessing and Cleaning
  3. Feature Analysis and Correlation
  4. Risk Score Calculation Model
  5. Default Probability Analysis
  6. Visualization of Risk Patterns
  7. Time Series Analysis of Default Patterns
  8. Machine Learning Model for Risk Prediction
  9. Financial Impact Analysis
  10. Risk Segmentation and Portfolio Strategy
  11. Early Warning System Implementation
  12. Risk-Adjusted Pricing Model
  13. Automated Decision Support System
  14. Performance Monitoring and Model Validation
  15. Additional Resources

Loading and Initial Data Exploration

In loan risk analysis, the first crucial step is loading and examining the dataset structure. We'll use pandas to read the loan data and perform initial exploration to understand the basic characteristics of our dataset, including data types and missing values.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load the loan dataset
loan_data = pd.read_csv('loan_data.csv')

# Display basic information about the dataset
print("Dataset Info:")
print(loan_data.info())

# Display first few rows and basic statistics
print("\nFirst 5 rows:")
print(loan_data.head())

# Get summary statistics
print("\nSummary Statistics:")
print(loan_data.describe())

# Check missing values
missing_values = loan_data.isnull().sum()
print("\nMissing Values:")
print(missing_values[missing_values > 0])

Data Preprocessing and Cleaning

Data preprocessing is essential for loan risk analysis. We'll handle missing values, encode categorical variables, and normalize numerical features to prepare our dataset for deeper analysis and modeling.

# Handle missing values
def preprocess_loan_data(df):
    # Fill numerical missing values with median
    numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns
    for col in numerical_columns:
        df[col].fillna(df[col].median(), inplace=True)
    
    # Fill categorical missing values with mode
    categorical_columns = df.select_dtypes(include=['object']).columns
    for col in categorical_columns:
        df[col].fillna(df[col].mode()[0], inplace=True)
    
    # Encode categorical variables
    from sklearn.preprocessing import LabelEncoder
    le = LabelEncoder()
    for col in categorical_columns:
        df[col] = le.fit_transform(df[col])
    
    # Normalize numerical features
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    df[numerical_columns] = scaler.fit_transform(df[numerical_columns])
    
    return df

# Apply preprocessing
clean_loan_data = preprocess_loan_data(loan_data.copy())
print("Preprocessed data sample:")
print(clean_loan_data.head())

Feature Analysis and Correlation

Understanding relationships between different loan features is critical for risk assessment. We'll create a correlation matrix and visualize important feature relationships to identify potential default indicators.

# Create correlation matrix
correlation_matrix = clean_loan_data.corr()

# Plot correlation heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Feature Correlation Matrix')
plt.tight_layout()
plt.show()

# Analyze key features correlation with loan default
default_correlations = correlation_matrix['loan_status'].sort_values(ascending=False)
print("\nFeature Correlations with Loan Default:")
print(default_correlations)

# Create scatter plots for highly correlated features
top_correlated = default_correlations.head(3)
for feature in top_correlated.index:
    plt.figure(figsize=(8, 6))
    plt.scatter(clean_loan_data[feature], clean_loan_data['loan_status'], alpha=0.5)
    plt.xlabel(feature)
    plt.ylabel('Loan Status')
    plt.title(f'{feature} vs Loan Status')
    plt.show()

Risk Score Calculation Model

Developing a comprehensive risk scoring system combines multiple financial indicators to generate a single risk metric. This model weighs factors like credit history, income, and debt-to-income ratio to produce a normalized risk score.

def calculate_risk_score(data):
    # Define feature weights based on correlation analysis
    weights = {
        'income': 0.3,
        'debt_to_income': -0.25,
        'credit_score': 0.25,
        'payment_history': 0.2
    }
    
    # Calculate component scores
    risk_components = {
        'income': (data['income'] - data['income'].min()) / 
                 (data['income'].max() - data['income'].min()),
        'debt_to_income': 1 - (data['debt_to_income'] - data['debt_to_income'].min()) /
                         (data['debt_to_income'].max() - data['debt_to_income'].min()),
        'credit_score': (data['credit_score'] - data['credit_score'].min()) /
                       (data['credit_score'].max() - data['credit_score'].min()),
        'payment_history': (data['payment_history'] - data['payment_history'].min()) /
                         (data['payment_history'].max() - data['payment_history'].min())
    }
    
    # Calculate weighted risk score
    risk_score = sum(weights[component] * risk_components[component] 
                    for component in weights.keys())
    
    # Normalize to 0-100 scale
    risk_score = (risk_score * 100).round(2)
    
    return risk_score

# Calculate risk scores
clean_loan_data['risk_score'] = calculate_risk_score(clean_loan_data)
print("Sample risk scores:")
print(clean_loan_data[['income', 'debt_to_income', 'credit_score', 
                       'payment_history', 'risk_score']].head())

Default Probability Analysis

Understanding the probability of default based on historical data patterns helps in making informed lending decisions. This analysis uses logistic regression to calculate default probabilities for loan applications.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

def calculate_default_probability(data):
    # Select features for default prediction
    features = ['risk_score', 'income', 'debt_to_income', 'credit_score']
    X = data[features]
    y = data['loan_status']
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                        random_state=42)
    
    # Train logistic regression model
    model = LogisticRegression(random_state=42)
    model.fit(X_train, y_train)
    
    # Calculate probabilities
    probabilities = model.predict_proba(X)[:, 1]
    
    # Model evaluation
    y_pred = model.predict(X_test)
    print("Model Performance:")
    print(classification_report(y_test, y_pred))
    
    return probabilities

# Calculate default probabilities
clean_loan_data['default_probability'] = calculate_default_probability(clean_loan_data)
print("\nSample default probabilities:")
print(clean_loan_data[['risk_score', 'default_probability']].head())

Visualization of Risk Patterns

Creating comprehensive visualizations helps identify patterns in loan default behavior and risk factors. These visualizations combine multiple risk indicators to provide insights into high-risk loan profiles.

import seaborn as sns
import matplotlib.pyplot as plt

# Create risk visualization dashboard
plt.figure(figsize=(15, 10))

# Risk Score Distribution
plt.subplot(2, 2, 1)
sns.histplot(data=clean_loan_data, x='risk_score', hue='loan_status', bins=30)
plt.title('Risk Score Distribution by Loan Status')

# Default Probability vs Risk Score
plt.subplot(2, 2, 2)
sns.scatterplot(data=clean_loan_data, x='risk_score', y='default_probability', 
                hue='loan_status', alpha=0.6)
plt.title('Default Probability vs Risk Score')

# Risk Factors Heat Map
plt.subplot(2, 2, 3)
risk_factors = ['income', 'debt_to_income', 'credit_score', 'payment_history']
sns.heatmap(clean_loan_data[risk_factors].corr(), annot=True, cmap='RdYlBu')
plt.title('Risk Factors Correlation')

# Default Rate by Income Bracket
plt.subplot(2, 2, 4)
income_bins = pd.qcut(clean_loan_data['income'], q=5)
default_by_income = clean_loan_data.groupby(income_bins)['loan_status'].mean()
default_by_income.plot(kind='bar')
plt.title('Default Rate by Income Bracket')
plt.tight_layout()
plt.show()

print("Risk Score Statistics:")
print(clean_loan_data['risk_score'].describe())

Time Series Analysis of Default Patterns

Analyzing temporal patterns in loan defaults helps identify seasonal trends and economic factors affecting default rates. This analysis uses time-based aggregation to reveal patterns in default behavior over different time periods.

# Convert date columns to datetime
clean_loan_data['issue_date'] = pd.to_datetime(clean_loan_data['issue_date'])
clean_loan_data.set_index('issue_date', inplace=True)

# Calculate monthly default rates
monthly_defaults = clean_loan_data.resample('M')['loan_status'].agg(['mean', 'count'])
monthly_defaults.columns = ['default_rate', 'loan_count']

# Time series visualization
plt.figure(figsize=(15, 8))
fig, ax1 = plt.subplots()

# Plot default rate
ax1.plot(monthly_defaults.index, monthly_defaults['default_rate'], 'b-', label='Default Rate')
ax1.set_xlabel('Date')
ax1.set_ylabel('Default Rate', color='b')
ax1.tick_params(axis='y', labelcolor='b')

# Plot loan count on secondary axis
ax2 = ax1.twinx()
ax2.plot(monthly_defaults.index, monthly_defaults['loan_count'], 'r-', label='Loan Count')
ax2.set_ylabel('Number of Loans', color='r')
ax2.tick_params(axis='y', labelcolor='r')

plt.title('Monthly Default Rates and Loan Volume Over Time')
plt.show()

# Calculate seasonal decomposition
from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(monthly_defaults['default_rate'], period=12)
decomposition.plot()
plt.tight_layout()
plt.show()

Machine Learning Model for Risk Prediction

Implementing a gradient boosting classifier to predict loan defaults with high accuracy. This model combines multiple features and provides feature importance analysis for risk assessment.

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import cross_val_score

def build_risk_prediction_model(data):
    # Prepare features
    feature_columns = ['risk_score', 'income', 'debt_to_income', 'credit_score', 
                      'payment_history', 'loan_amount']
    X = data[feature_columns]
    y = data['loan_status']
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                        random_state=42)
    
    # Train model
    model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, 
                                     max_depth=3, random_state=42)
    model.fit(X_train, y_train)
    
    # Calculate feature importance
    importance = pd.DataFrame({
        'feature': feature_columns,
        'importance': model.feature_importances_
    }).sort_values('importance', ascending=False)
    
    # Model evaluation
    y_pred_proba = model.predict_proba(X_test)[:, 1]
    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
    roc_auc = auc(fpr, tpr)
    
    # Cross-validation
    cv_scores = cross_val_score(model, X, y, cv=5)
    
    return model, importance, (fpr, tpr, roc_auc), cv_scores

# Train model and get results
model, importance, roc_metrics, cv_scores = build_risk_prediction_model(clean_loan_data)

# Plot feature importance
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=importance)
plt.title('Feature Importance in Risk Prediction')
plt.show()

# Plot ROC curve
plt.figure(figsize=(8, 6))
plt.plot(roc_metrics[0], roc_metrics[1], label=f'ROC curve (AUC = {roc_metrics[2]:.2f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve for Risk Prediction Model')
plt.legend()
plt.show()

print(f"Cross-validation scores (mean ± std): {cv_scores.mean():.3f} ± {cv_scores.std():.3f}")

Financial Impact Analysis

Analyzing the financial implications of default predictions helps optimize lending strategies. This model calculates expected losses and potential revenue impacts based on risk predictions and loan characteristics.

def analyze_financial_impact(data, predictions):
    # Calculate expected loss rate based on risk score
    data['expected_loss_rate'] = data['default_probability'] * 0.65  # Assuming 65% loss given default
    
    # Calculate potential losses
    data['loan_loss'] = data['loan_amount'] * data['expected_loss_rate']
    
    # Calculate risk-adjusted return
    data['interest_rate'] = 0.05 + (data['risk_score'] / 100 * 0.15)  # Base rate + risk premium
    data['expected_return'] = data['loan_amount'] * (
        (1 - data['default_probability']) * data['interest_rate'] - 
        data['default_probability'] * 0.65
    )
    
    # Portfolio analysis
    portfolio_metrics = {
        'total_loan_amount': data['loan_amount'].sum(),
        'expected_total_loss': data['loan_loss'].sum(),
        'expected_return': data['expected_return'].sum(),
        'risk_adjusted_return': data['expected_return'].mean() / data['loan_loss'].std()
    }
    
    # Visualize risk-return relationship
    plt.figure(figsize=(12, 6))
    
    plt.subplot(1, 2, 1)
    plt.scatter(data['risk_score'], data['expected_return'], alpha=0.5)
    plt.xlabel('Risk Score')
    plt.ylabel('Expected Return')
    plt.title('Risk-Return Profile')
    
    plt.subplot(1, 2, 2)
    sns.boxplot(x=pd.qcut(data['risk_score'], 5, labels=['Very Low', 'Low', 'Medium', 'High', 'Very High']),
                y='loan_loss')
    plt.xlabel('Risk Category')
    plt.ylabel('Expected Loss')
    plt.title('Loss Distribution by Risk Category')
    
    plt.tight_layout()
    plt.show()
    
    return portfolio_metrics

# Calculate financial metrics
portfolio_results = analyze_financial_impact(clean_loan_data, clean_loan_data['default_probability'])
print("\nPortfolio Metrics:")
for metric, value in portfolio_results.items():
    print(f"{metric}: ${value:,.2f}")

Risk Segmentation and Portfolio Strategy

Developing a strategic approach to portfolio management through risk segmentation helps optimize lending decisions and maintain a balanced risk profile across different borrower segments.

def segment_loan_portfolio(data):
    # Create risk segments
    data['risk_category'] = pd.qcut(data['risk_score'], 
                                  q=5, 
                                  labels=['Very High Risk', 'High Risk', 
                                        'Medium Risk', 'Low Risk', 'Very Low Risk'])
    
    # Calculate segment metrics
    segment_analysis = data.groupby('risk_category').agg({
        'loan_amount': ['count', 'sum', 'mean'],
        'default_probability': 'mean',
        'expected_return': 'mean',
        'loan_loss': 'sum'
    }).round(2)
    
    # Calculate recommended portfolio allocation
    total_portfolio = data['loan_amount'].sum()
    recommended_allocation = {
        'Very Low Risk': 0.35,
        'Low Risk': 0.30,
        'Medium Risk': 0.20,
        'High Risk': 0.10,
        'Very High Risk': 0.05
    }
    
    # Visualize current vs recommended allocation
    current_allocation = data.groupby('risk_category')['loan_amount'].sum() / total_portfolio
    
    plt.figure(figsize=(12, 6))
    width = 0.35
    x = np.arange(len(recommended_allocation))
    
    plt.bar(x - width/2, current_allocation, width, label='Current Allocation')
    plt.bar(x + width/2, list(recommended_allocation.values()), width, label='Recommended Allocation')
    
    plt.xlabel('Risk Category')
    plt.ylabel('Portfolio Share')
    plt.title('Current vs Recommended Portfolio Allocation')
    plt.xticks(x, recommended_allocation.keys(), rotation=45)
    plt.legend()
    plt.tight_layout()
    plt.show()
    
    return segment_analysis, recommended_allocation

# Perform segmentation analysis
segment_metrics, recommended_alloc = segment_loan_portfolio(clean_loan_data)
print("\nSegment Analysis:")
print(segment_metrics)

Early Warning System Implementation

Implementing an early warning system to detect potential defaults before they occur by monitoring key behavioral indicators and payment patterns. This system helps in proactive risk management and early intervention.

def implement_early_warning_system(data):
    # Define warning indicators
    def calculate_warning_score(row):
        warning_score = 0
        
        # Payment behavior indicators
        if row['late_payment_count'] > 2:
            warning_score += 25
        if row['payment_amount_reduction'] > 0.1:
            warning_score += 15
            
        # Financial health indicators
        if row['debt_to_income'] > 0.5:
            warning_score += 20
        if row['savings_reduction_rate'] > 0.25:
            warning_score += 15
            
        # Credit behavior changes
        if row['credit_utilization'] > 0.8:
            warning_score += 25
            
        return warning_score

    # Calculate warning scores
    data['warning_score'] = data.apply(calculate_warning_score, axis=1)
    
    # Define risk levels
    data['risk_level'] = pd.cut(data['warning_score'], 
                               bins=[0, 20, 40, 60, 80, 100],
                               labels=['Very Low', 'Low', 'Medium', 'High', 'Very High'])
    
    # Generate warnings
    high_risk_cases = data[data['warning_score'] >= 60].sort_values('warning_score', 
                                                                   ascending=False)
    
    # Visualize warning distribution
    plt.figure(figsize=(12, 6))
    
    plt.subplot(1, 2, 1)
    sns.histplot(data=data, x='warning_score', bins=20)
    plt.title('Distribution of Warning Scores')
    plt.xlabel('Warning Score')
    plt.ylabel('Count')
    
    plt.subplot(1, 2, 2)
    risk_level_counts = data['risk_level'].value_counts().sort_index()
    plt.pie(risk_level_counts, labels=risk_level_counts.index, autopct='%1.1f%%')
    plt.title('Risk Level Distribution')
    
    plt.tight_layout()
    plt.show()
    
    return high_risk_cases

# Implement early warning system
high_risk_loans = implement_early_warning_system(clean_loan_data)
print("\nHigh Risk Cases Requiring Immediate Attention:")
print(high_risk_loans[['warning_score', 'risk_level', 'loan_amount']].head())

Risk-Adjusted Pricing Model

Developing a dynamic pricing model that adjusts interest rates based on calculated risk scores and market conditions to optimize risk-return trade-offs while maintaining competitiveness.

def calculate_risk_adjusted_pricing(data):
    # Base rate components
    base_rate = 0.05  # 5% base rate
    
    def calculate_risk_premium(row):
        # Risk premium based on risk score
        risk_premium = (100 - row['risk_score']) / 100 * 0.15
        
        # Adjust for market factors
        market_adjustment = 0.01 if row['loan_amount'] > 50000 else 0
        competition_adjustment = -0.005 if row['credit_score'] > 750 else 0
        
        return risk_premium + market_adjustment + competition_adjustment
    
    # Calculate final rates
    data['risk_premium'] = data.apply(calculate_risk_premium, axis=1)
    data['final_rate'] = base_rate + data['risk_premium']
    
    # Calculate expected returns
    data['expected_yearly_return'] = data['loan_amount'] * data['final_rate'] * \
                                   (1 - data['default_probability'])
    
    # Visualize pricing distribution
    plt.figure(figsize=(15, 5))
    
    plt.subplot(1, 3, 1)
    sns.scatterplot(data=data, x='risk_score', y='final_rate')
    plt.title('Risk Score vs Interest Rate')
    
    plt.subplot(1, 3, 2)
    sns.boxplot(x=pd.qcut(data['risk_score'], 5, labels=['VH', 'H', 'M', 'L', 'VL']),
                y='final_rate')
    plt.title('Rate Distribution by Risk Category')
    
    plt.subplot(1, 3, 3)
    sns.regplot(data=data, x='final_rate', y='expected_yearly_return')
    plt.title('Rate vs Expected Return')
    
    plt.tight_layout()
    plt.show()
    
    return data[['risk_score', 'risk_premium', 'final_rate', 'expected_yearly_return']]

# Calculate risk-adjusted prices
pricing_results = calculate_risk_adjusted_pricing(clean_loan_data)
print("\nRisk-Adjusted Pricing Summary:")
print(pricing_results.describe())

Automated Decision Support System

Creating an automated system that combines all previous analyses to provide standardized lending recommendations. This system integrates risk scores, financial metrics, and early warning indicators to generate consistent decision suggestions.

def automated_decision_system(applicant_data):
    # Decision thresholds
    RISK_THRESHOLD = 70
    DTI_THRESHOLD = 0.43
    CREDIT_SCORE_THRESHOLD = 640
    
    def calculate_decision_score(data):
        # Weighted scoring system
        weights = {
            'risk_score': 0.35,
            'credit_score': 0.25,
            'debt_to_income': 0.20,
            'payment_history': 0.20
        }
        
        normalized_scores = {
            'risk_score': data['risk_score'] / 100,
            'credit_score': data['credit_score'] / 850,
            'debt_to_income': 1 - (data['debt_to_income'] / 0.6),
            'payment_history': data['payment_history']
        }
        
        return sum(weights[k] * normalized_scores[k] for k in weights.keys())

    # Calculate decision scores
    applicant_data['decision_score'] = applicant_data.apply(calculate_decision_score, axis=1)
    
    # Generate recommendations
    def get_recommendation(row):
        if row['decision_score'] >= 0.8:
            return 'Approve - Standard Rate'
        elif row['decision_score'] >= 0.6:
            return 'Approve - Higher Rate'
        elif row['decision_score'] >= 0.4:
            return 'Conditional Approval'
        else:
            return 'Deny'
    
    applicant_data['recommendation'] = applicant_data.apply(get_recommendation, axis=1)
    
    # Visualize decision distribution
    plt.figure(figsize=(15, 5))
    
    plt.subplot(1, 3, 1)
    sns.histplot(data=applicant_data, x='decision_score', bins=30)
    plt.title('Decision Score Distribution')
    
    plt.subplot(1, 3, 2)
    recommendations = applicant_data['recommendation'].value_counts()
    plt.pie(recommendations, labels=recommendations.index, autopct='%1.1f%%')
    plt.title('Recommendation Distribution')
    
    plt.subplot(1, 3, 3)
    sns.scatterplot(data=applicant_data, x='risk_score', y='decision_score', 
                    hue='recommendation')
    plt.title('Risk Score vs Decision Score')
    
    plt.tight_layout()
    plt.show()
    
    return applicant_data[['decision_score', 'recommendation', 'risk_score', 
                          'credit_score', 'debt_to_income']]

# Generate automated decisions
decision_results = automated_decision_system(clean_loan_data)
print("\nDecision System Results:")
print(decision_results.head())
print("\nDecision Distribution:")
print(decision_results['recommendation'].value_counts(normalize=True))

Performance Monitoring and Model Validation

Implementing a comprehensive monitoring system to track model performance, validate predictions, and ensure the risk assessment system maintains its effectiveness over time.

def monitor_model_performance(predictions, actuals, time_periods):
    # Calculate performance metrics over time
    def calculate_period_metrics(pred, act):
        from sklearn.metrics import precision_score, recall_score, f1_score
        return {
            'precision': precision_score(act, pred > 0.5),
            'recall': recall_score(act, pred > 0.5),
            'f1': f1_score(act, pred > 0.5),
            'default_rate': act.mean()
        }
    
    # Track metrics over time
    performance_history = []
    for period in time_periods:
        period_mask = (predictions.index >= period[0]) & (predictions.index < period[1])
        metrics = calculate_period_metrics(predictions[period_mask], 
                                        actuals[period_mask])
        metrics['period'] = period[0]
        performance_history.append(metrics)
    
    performance_df = pd.DataFrame(performance_history)
    
    # Plot performance trends
    plt.figure(figsize=(15, 5))
    
    plt.subplot(1, 3, 1)
    plt.plot(performance_df['period'], performance_df['precision'], marker='o')
    plt.title('Precision Over Time')
    plt.xticks(rotation=45)
    
    plt.subplot(1, 3, 2)
    plt.plot(performance_df['period'], performance_df['recall'], marker='o', color='orange')
    plt.title('Recall Over Time')
    plt.xticks(rotation=45)
    
    plt.subplot(1, 3, 3)
    plt.plot(performance_df['period'], performance_df['default_rate'], marker='o', color='green')
    plt.title('Default Rate Over Time')
    plt.xticks(rotation=45)
    
    plt.tight_layout()
    plt.show()
    
    return performance_df

# Generate time periods for monitoring
time_periods = [(pd.Timestamp('2023-01-01') + pd.DateOffset(months=i),
                pd.Timestamp('2023-01-01') + pd.DateOffset(months=i+1))
               for i in range(12)]

# Monitor model performance
performance_metrics = monitor_model_performance(clean_loan_data['default_probability'],
                                             clean_loan_data['loan_status'],
                                             time_periods)
print("\nPerformance Monitoring Results:")
print(performance_metrics)

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