Credit Risk Prediction Using Machine Learning

Project Overview & Business Context

Credit risk assessment is a cornerstone of every financial institution. Lenders face major financial losses when loans are given to applicants who default. Traditionally, credit analysts rely on manual reviews of applications a process that’s time-consuming, subjective, and prone to inconsistency.

This project aimed to automate and modernize credit risk evaluation using machine learning.
The goal was to predict whether a loan applicant is likely to be a Good Credit (low risk) or Bad Credit (high risk) borrower, enabling institutions to make faster, fairer, and data-driven decisions.

This project combines financial reasoning with data science blending model interpretability, business logic, and responsible automation.

Problem Statement

Can a machine learning model classify an applicant’s credit risk level (good/bad) as reliably or better than traditional manual methods?

Key Goals:

• ⏱️ Reduce loan application processing time
• 🧩 Minimize human bias and subjectivity
• ⚖️ Ensure fairness and consistency in decisions
• 💡 Lower default rates through smarter, data-backed predictions

Dataset Description & Features

The dataset used is the German Credit Risk Dataset, a benchmark dataset widely applied in financial ML research.
Each record represents a loan applicant with multiple attributes such as age, job, savings, housing, and loan amount.

Feature	Description
Age	Applicant’s age (proxy for earning stability and maturity)
Sex	Gender as recorded in the application
Job	Occupation skill level (0–3, higher = lower risk)
Housing	Living situation (own, rent, free)
Saving Accounts	Categories indicating liquidity (“little”, “moderate”, “rich”)
Checking Account	Active account status with balance range
Credit Amount	Loan amount requested
Duration	Loan term in months
Purpose	Reason for loan (e.g., car, education, furniture)

Target Variable:

• 1 → Good Credit (Low Risk)
• 0 → Bad Credit (High Risk)

Tools, Technologies & Environment

Category	Tools/Frameworks
Language	Python
Libraries	Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Modeling	Decision Tree, Random Forest, Extra Trees
Preprocessing	LabelEncoder, Joblib
Visualization	Seaborn, Matplotlib
Deployment	Streamlit + Ngrok
Environment	Google Colab

Data Preprocessing & Feature Engineering

Handling Missing Values:

• Missing entries (e.g., savings/checking accounts) were filled using mode imputation to retain data integrity.

Encoding Categorical Data:

• Used LabelEncoder for categorical columns.
• Created and saved individual encoders for consistent category mapping between training and deployment stages.

Scaling & Transformation:

• Skipped scaling to maintain interpretability and to leverage ExtraTreesClassifier’s scale-invariance.

Model Building & Selection

Initial exploration included Decision Tree and Random Forest models.
The final choice was ExtraTreesClassifier due to its superior accuracy and interpretability.

Why ExtraTrees?

• Reduces overfitting via decorrelated tree ensembles
• Handles mixed feature types naturally
• Provides feature importance ranking vital for explainability in banking

Hyperparameter Tuning:

• Used GridSearchCV with 5-fold cross-validation
• Tuned parameters like n_estimators, max_depth, and min_samples_split

Model Evaluation & Metrics

Metric	Value	Interpretation
Accuracy	~83%	Majority of predictions correct
Precision	0.85	Most “good” predictions are actually good
Recall	0.81	Most “good” customers correctly identified
F1-Score	0.83	Balanced performance
Confusion Matrix	Balanced	No bias toward any class

Business Outcome:
The model effectively minimized both false approvals (bad loans) and false rejections (good applicants), ensuring fair and practical real-world performance.

Model Saving, Pipeline, and Consistency

• Both the trained model and label encoders were serialized using Joblib
• Maintained a consistent pipeline from training to prediction
• Prevented data transformation drift between environments
• Ensured future integration as a microservice or API is seamless

Streamlit Application & Deployment

A Streamlit app was developed to simulate real-world banking use.

App Features:

• Clean and interactive interface
• Input fields mirroring actual loan applications
• Instant prediction output: “Good Credit” or “Bad Credit”
• Reuse of trained encoders for consistent transformation

Deployment:

• Hosted via Google Colab + Ngrok for live public demo access
• Enabled zero-infrastructure, cloud-based testing

Real-World Impact

Area	Impact
Speed	Reduced decision time from days → seconds
Fairness	Eliminated subjective bias
Compliance	Provided transparent, explainable outputs
Scalability	Can process 1,000+ applications/minute
Profitability	Lowered default rates and boosted lending efficiency

Studies show ML adoption in lending can reduce default rates by up to 40% and cut onboarding time by 50%.

Challenges Faced & Solutions

Challenge	Solution
Encoder mismatch between train/test	Saved unique encoders per feature using Joblib
Hyperparameter tuning errors	Debugged GridSearchCV logs carefully
Ngrok authentication issues	Resolved with proper token setup and validation
Data type inconsistencies	Implemented schema validation and clean encoding

Learning Outcomes & Insights

Area	Key Takeaway
Data Quality	Even minor formatting errors can break entire pipelines
Feature Engineering	Consistency in encoding ensures production reliability
Model Selection	Ensemble models outperform individual learners
Usability	Streamlit simplifies ML adoption for non-technical users
Deployment	Saving full pipelines is critical for live stability

Conclusion & Future Scope

This project demonstrates an end-to-end ML workflow — from data exploration to deployment.
It reflects how AI-driven credit scoring can reshape financial decision-making by making it faster, unbiased, and explainable.

Future Enhancements:

• Integrate Explainable AI (XAI) for transparency
• Deploy on AWS / Heroku for permanent hosting
• Add real-time dashboard for loan portfolio monitoring

A complete production-ready showcase of Data Science applied to FinTech balancing business sense, model integrity, and practical usability.

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Ibrahim Shaikh
📍 Data Science & Analyst
📧 ibrahimsh1910@gmail.com
💼 LinkedIn Profile

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