Loan Default Risk Assessment XGBoost Credit Scoring Demo

Enter basic applicant and loan information on the left. The model estimates the probability that the loan will be repaid on time.

Model version: v1.0 · Educational demo only
Training data: anonymized historical loan records

Applicant & Loan Information

Adjust the fields below manually or use the demo button to quickly generate a random example case.

Very young borrowers often carry slightly higher risk.
Higher income generally implies stronger repayment ability.
Higher scores indicate better credit history.
Existing monthly debt payments / monthly income.
Larger amounts make lenders more sensitive to risk.
Longer terms mean more uncertainty over time.
Used as a categorical feature in the model.
Different purposes historically show different risk levels.
More serious past delinquencies strongly increase risk.

This page is for academic and demonstration purposes only. It should not be used for real-world credit decisions.

Predicted outcome
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Waiting for input
Model: XGBoost-style credit scoring demo
High default risk Moderate Low default risk
Binary target: repaid vs. default Evaluation: AUC / confusion matrix Features: income, DTI, credit score, etc.
Automatic interpretation (simplified)
  • After you enter (or auto-fill) the inputs, the model will highlight several key factors that push risk up or down.

The explanations below approximate typical behavior of a credit scoring model and are meant to support your project presentation.