Loan Default Risk Analysis
Explore borrower characteristics and design a structured workflow for predicting loan default risk.
BUSINESS CONTEXT
Why this project matters
A lending team wants a more structured way to identify risky applications before approval. They need both exploratory insight into borrower behavior and a defensible modeling workflow that reflects the cost of bad lending decisions.
PROJECT OBJECTIVE
What you are expected to accomplish
Analyze borrower and loan features, identify the strongest default-risk patterns, and outline or build a classification workflow with metrics suited to lending decisions.
DATASET OVERVIEW
Expected dataset structure
Expect borrower and loan-level data covering demographics, income, credit history, repayment behavior, loan terms, and whether the borrower defaulted.
Main entities / rows
One row per loan application or funded loan
Target variable
defaulted (Yes/No)
loan_id
stringUnique identifier for the loan record.
annual_income
floatReported borrower income, potentially missing or skewed.
employment_length
categoricalBorrower employment tenure bucket or years.
credit_score_band
categoricalGrouped creditworthiness measure.
debt_to_income_ratio
floatDebt burden relative to income.
loan_amount
floatRequested or approved principal amount.
loan_purpose
categoricalReason for the loan, such as education or debt consolidation.
defaulted
binaryTarget indicating whether the borrower defaulted.
Data quality issues to expect
- Income and employment history may be missing or inconsistently formatted.
- Default events are often less common than non-defaults, creating class imbalance.
- Some variables may only be available after approval or after repayment begins, which creates leakage risk.
QUESTIONS TO ANSWER
Focus the work around meaningful decisions
- 01
Which borrower characteristics are most strongly associated with default?
- 02
How do income, credit score, debt burden, and loan purpose relate to risk?
- 03
Is the default rate imbalanced enough to make accuracy misleading?
- 04
Which engineered features could make risk patterns clearer or more predictive?
- 05
What evaluation strategy would be appropriate for a lending use case?
PROJECT ROADMAP
Move through the case study in a practical sequence
- 01
Step 01
Understand the lending decision
Clarify what default means and how predictions would be used in practice.
- 02
Step 02
Audit the loan dataset
Inspect class balance, data types, missingness, and candidate leakage fields.
- 03
Step 03
Clean borrower records
Handle inconsistent employment, income gaps, and noisy categorical labels.
- 04
Step 04
Perform risk-focused EDA
Compare default rates across borrower, loan, and debt profiles.
- 05
Step 05
Engineer risk features
Create bins, ratios, or grouped features that reflect credit behavior more clearly.
- 06
Step 06
Model and evaluate carefully
Choose metrics and thresholds that reflect the cost of risky false negatives.
- 07
Step 07
Summarize a credit-risk workflow
Document findings, caveats, and a practical next step for the lending team.
TASKS / MILESTONES
Concrete work to complete
- Measure the default rate and assess class imbalance.
- Inspect missing values in income, employment, and credit-related features.
- Compare default by credit score band, loan purpose, and debt-to-income range.
- Create at least two useful engineered features or grouped variables.
- Train a baseline classifier or outline one clearly if modeling is out of scope.
- Evaluate results with metrics beyond accuracy and explain why they matter.
- Document any leakage risks or unrealistic features you exclude.
SUGGESTED VISUALIZATIONS
Visuals worth creating
- Default-rate bar chart by loan purpose
- Credit-score band versus default comparison
- Debt-to-income distribution by default status
- Confusion matrix for the chosen model threshold
- Precision-recall curve or metric comparison table
DELIVERABLES
What the learner should produce
- Cleaned borrower-loan dataset
- Exploratory risk analysis notebook
- Feature-engineering notes
- Baseline model or evaluation workflow
- Risk findings and lending recommendations
SUCCESS CRITERIA
What strong completion looks like
- The workflow distinguishes analysis-ready fields from leakage-prone fields.
- Default-risk patterns are explained with both visuals and business reasoning.
- Evaluation choices reflect lending tradeoffs instead of relying on accuracy alone.
- The final output makes clear what a stakeholder should trust and what still needs validation.
GUIDANCE
Support when you need a nudge
Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.
Complete this project when the work is yours
Use this only after you have worked through the roadmap, milestones, and your own analysis.
KEY LEARNING OUTCOMES
Skills reinforced by this project
- Audit a financial dataset for leakage, imbalance, and data-quality issues.
- Use EDA and feature engineering to surface borrower-risk patterns.
- Select classification metrics that match lending risk tradeoffs.
- Present a modeling workflow with realistic caveats.
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