Credit Risk Prediction
End-to-end ML system for predicting borrower default risk using financial, demographic, and credit-history attributes. Binary classification with structured preprocessing, probability estimation, explainability signals, and decision-oriented risk bands for lending workflows.
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Why this project matters
Credit risk models convert borrower information into measurable default probability. That makes lending decisions more consistent, scalable, and explainable than manual review alone.
Implementation profile
The project is presented as a production-oriented scoring workflow rather than a notebook-only experiment.
Credit risk in lending
Lending institutions need to estimate the probability that a borrower will default. A calibrated probability supports underwriting, pricing, manual review policies, and aggregation of portfolio-level risk.
Why probability matters
Risk probability supports decision thresholds, pricing by risk tier, and downstream portfolio analytics beyond a simple yes or no label.
Explainability
Top drivers and feature importance help internal stakeholders and regulators understand why an applicant was flagged as higher risk.
Use cases
Typical applications include applicant screening, portfolio stress testing, pricing logic, and prioritization for manual review.
Borrower and loan attributes
The dataset combines demographic, loan, and credit-history features commonly seen in real scoring pipelines.
Positive class definition
The positive class is the event we want to flag: higher credit risk or potential default. This matters because precision, recall, and threshold policy all depend on which class is treated as positive.
From applicant data to default probability
The system predicts whether an applicant belongs to the higher-risk group, then turns that score into business-usable probability bands such as low, medium, or high risk.
Collect borrower features
Use structured demographic, loan, and credit-history inputs from the application and bureau-style signals.
Estimate higher-risk probability
Generate a calibrated probability in the range [0, 1] rather than a raw class label only.
Apply decision thresholds
Translate scores into operational actions such as approve, manual review, or decline.
Expose reasons
Provide top risk factors and importance signals to support explainability and communication.
Structured and deterministic input pipeline
Reliable risk scoring depends on consistent feature handling. The preprocessing layer validates the input schema, imputes missing values, encodes categories, and scales numeric features.
Schema validation
Ensure required fields such as person_*, loan_*, cb_*, and loan_status exist in expected structure.
Missing values
Impute numeric features with median and categorical features with most-frequent values.
Categorical encoding
Use OneHotEncoder for person_home_ownership, loan_intent, loan_grade, and default flags.
Scaling
Apply StandardScaler to numeric features to stabilize optimization and preserve pipeline consistency.
Risk signal categories
The model combines multiple signal families so that default risk is not inferred from a single isolated feature.
Demographic
person_age, person_income, person_emp_length, person_home_ownership
Loan characteristics
loan_intent, loan_grade, loan_amnt, loan_int_rate, loan_percent_income
Credit history
cb_person_default_on_file, cb_person_cred_hist_length
Why these groups matter
Together these features approximate repayment capacity, loan burden, borrowing context, and historical credit behavior.
Balancing interpretability and performance
A simple baseline such as logistic regression establishes a performance floor, while tree-based models often improve ROC-AUC and produce native feature-importance signals.
Operational choices
Classification quality from multiple perspectives
Credit risk models should not be evaluated with a single metric. High precision reduces false alarms, recall avoids missing risky applicants, and ROC-AUC measures ranking quality across thresholds.
ROC-AUC
0.941 Ranking quality across thresholds for imbalanced binary classification.
Precision
0.973 Of predicted high-risk cases, how many were actually risky.
Recall
0.708 Of all risky applicants, how many the model successfully flags.
F1 Score
0.820 Single summary metric balancing precision and recall.
Top drivers of default risk
Feature-importance analysis highlights the strongest signals that push the model toward higher-risk predictions.
Threshold behavior and class outcomes
The ROC curve summarizes ranking power across thresholds, while the confusion matrix shows what happens once a specific decision threshold is chosen.
ROC curve
Turning model output into lending decisions
A useful credit model does more than classify. It supports clear operational rules and stakeholder communication.
Risk bands
Use low, medium, and high-risk bands to route applications into auto-approval, manual review, or tighter underwriting.
Top factors
Feature-level explanations help justify decisions and show which borrower attributes drove the elevated risk score.
Portfolio use
Calibrated probabilities support portfolio aggregation, concentration monitoring, and scenario-based stress testing.
Estimate borrower default risk
Enter borrower and loan features below to simulate a production-style credit scoring request. The result includes a default probability, risk band, and top risk factors.
How the risk scoring workflow operates
The case study is structured like a deployable ML system rather than a static report. The frontend consumes saved metrics and exposes a live scoring interface backed by the same preprocessing and model pipeline used in training.