Loan Default Risk Analysis
OVERVIEW
This project analyzes loan default patterns using machine learning on historical financial data. The goal was to identify high-risk borrowers early, highlight drivers of default, and deliver actionable insights that financial institutions could use to mitigate risk.
ROLE
From Data Wrangling to Deployment
As Data Engineer, I led the end-to-end lifecycle of this project: data preprocessing, feature engineering, model selection, evaluation, and recommendations.
I combined technical expertise in Python, SQL, and Jupyter with data visualization in Tableau to balance predictive accuracy with interpretability.
Key Dimensions
Data Engineering
Cleaned, preprocessed, and engineered features from financial datasets
Model Development
Built and validated predictive models using machine learning algorithms
Risk Analysis
Translated technical findings into business recommendations for risk mitigation
KEY CHALLENGES
Navigating Financial Complexity
Imbalanced Dataset
Dataset contained far fewer defaults than non-defaults, requiring specialized techniques to ensure model could effectively identify high-risk cases.
Feature Correlations
Complex relationships between financial variables and noise in payment history data affected model stability and interpretability.
Precision vs Recall
Need for both high recall (catch defaults) and high precision (avoid false positives) to balance risk detection with customer experience.
DATA PREPARATION
From Raw Data to Model-Ready Inputs
Data Cleaning
Cleaned missing and inconsistent payment data, handling null values and standardizing financial metrics across the dataset.
Class Imbalance
Applied SMOTE (Synthetic Minority Oversampling) to address class imbalance and improve model's ability to detect defaults.
Feature Engineering
Engineered new features including repayment history flags and credit utilization ratios to capture risk patterns.
Tech Stack
Python
Pandas
Scikit-learn
JupyterMODELING
Balancing Interpretability and Accuracy
I trained multiple models including Logistic Regression and Random Forest. Logistic Regression offered interpretability, while Random Forest delivered higher predictive power.
Logistic Regression
Random Forest
RESULTS
Model Performance Insights
Random Forest Winner
Random Forest model outperformed others overall, striking the best balance between recall and precision for deployment.
Interpretability Maintained
Logistic Regression provided valuable interpretability for stakeholders, ensuring trust in model decisions.
Balanced Performance
Both models achieved strong F1 scores of 0.94, demonstrating effective handling of the precision-recall tradeoff.
INSIGHTS
Key Technical Discoveries
SMOTE Enhancement
Improved detection of high-risk borrowers by boosting recall through synthetic minority oversampling techniques.
Model Selection
Random Forest chosen for deployment, Logistic Regression kept for explainability and stakeholder confidence.
Future Development
Fine-tuning hyperparameters and testing XGBoost for further performance gains in precision and recall balance.
BUSINESS RECOMMENDATIONS
Turning Predictions into Action
Early Intervention
Flag borrowers with early payment delays (PAY_0, PAY_2) for proactive outreach and support programs.
Action: Implement automated alert system for payment delays
Credit Limit Adjustments
Apply stricter checks for borrowers with low limits and repayment issues to prevent over-extension.
Action: Deploy dynamic credit limit adjustment based on risk scores
Payment Behavior Monitoring
Real-time alert system for repeated late payments to enable immediate intervention strategies.
Action: Build dashboard for payment pattern monitoring
Risk-Based Interest Rates
Dynamic loan rates tied to default probability scores to better price risk and improve profitability.
Action: Integrate risk scoring into pricing algorithms
RISK MITIGATION THROUGH DATA
From Prediction to Protection
This project demonstrated the power of machine learning to reduce financial risk and enhance lending practices. By blending technical rigor with business strategy, I delivered a predictive framework that not only identified risk but also informed actionable strategies to reduce defaults.
Technical Achievement
- Model Performance:Achieved 97% precision with Random Forest while maintaining strong recall for default detection.
- Data Engineering:Successfully handled class imbalance and engineered meaningful features from financial data.
- Interpretability:Balanced model accuracy with explainability for stakeholder confidence and regulatory compliance.
Business Impact
- Risk Reduction:Provided early warning system for high-risk borrowers to enable proactive intervention.
- Strategic Framework:Delivered actionable recommendations for credit policies and risk-based pricing strategies.
- Scalable Solution:Created reusable methodology for ongoing risk assessment and model improvement.
By combining machine learning expertise with financial domain knowledge, this project transformed raw data into a strategic asset for risk management and business growth.