Predict Loan Default with Logistic Regression

Easy

Machine Learning

Asked at 1 company1Supervised LearningCross-ValidationFeature Engineering

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Problem

Business Context

LendWise, a mid-size digital lending platform processing about 120K personal loan applications per quarter, wants a model to predict whether an approved borrower will default within 12 months. The risk team needs a simple, explainable model to support underwriting decisions and reduce charge-offs without materially lowering approval volume.

Dataset

The training data contains historical funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit attributes	9	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Loan attributes	7	loan_amount, interest_rate, term_months, purpose
Banking and income	6	annual_income, debt_to_income, monthly_obligations, verified_income
Behavioral / history	5	prior_loans, prior_defaults, days_since_last_loan, autopay_enrolled

Rows: 240K funded loans, 33 input features
Target: default_12m — whether the borrower becomes 90+ days delinquent within 12 months
Class balance: 11.5% default, 88.5% non-default
Missing data: ~8% missing in employment and income verification fields; ~3% missing in revolving utilization

Success Criteria

A good solution should achieve ROC-AUC >= 0.78, recall >= 0.60 at precision >= 0.35, and provide coefficients or feature effects that the credit policy team can explain to auditors.

Constraints

The model must be interpretable enough for regulated lending review.
Batch scoring should complete in under 10 minutes for 150K applications.
Retraining should be feasible monthly with limited ML ops support.

Deliverables

Build a binary classification model to predict 12-month default.
Explain why the chosen algorithm fits this business problem.
Describe preprocessing for missing values, categorical variables, and skewed numeric features.
Evaluate the model using threshold-free and threshold-based metrics.
Recommend a decision threshold for underwriting based on business tradeoffs.

Problem

Business Context

Dataset

The training data contains historical funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit attributes	9	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Loan attributes	7	loan_amount, interest_rate, term_months, purpose
Banking and income	6	annual_income, debt_to_income, monthly_obligations, verified_income
Behavioral / history	5	prior_loans, prior_defaults, days_since_last_loan, autopay_enrolled

Rows: 240K funded loans, 33 input features
Target: default_12m — whether the borrower becomes 90+ days delinquent within 12 months
Class balance: 11.5% default, 88.5% non-default
Missing data: ~8% missing in employment and income verification fields; ~3% missing in revolving utilization

Success Criteria

A good solution should achieve ROC-AUC >= 0.78, recall >= 0.60 at precision >= 0.35, and provide coefficients or feature effects that the credit policy team can explain to auditors.

Constraints

The model must be interpretable enough for regulated lending review.
Batch scoring should complete in under 10 minutes for 150K applications.
Retraining should be feasible monthly with limited ML ops support.

Deliverables

Build a binary classification model to predict 12-month default.
Explain why the chosen algorithm fits this business problem.
Describe preprocessing for missing values, categorical variables, and skewed numeric features.
Evaluate the model using threshold-free and threshold-based metrics.
Recommend a decision threshold for underwriting based on business tradeoffs.

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Business Context

Dataset

The training data contains historical funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit attributes	9	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Loan attributes	7	loan_amount, interest_rate, term_months, purpose
Banking and income	6	annual_income, debt_to_income, monthly_obligations, verified_income
Behavioral / history	5	prior_loans, prior_defaults, days_since_last_loan, autopay_enrolled

Rows: 240K funded loans, 33 input features

Target: default_12m — whether the borrower becomes 90+ days delinquent within 12 months

Class balance: 11.5% default, 88.5% non-default

Missing data: ~8% missing in employment and income verification fields; ~3% missing in revolving utilization

Deliverables

Build a binary classification model to predict 12-month default.

Explain why the chosen algorithm fits this business problem.

Describe preprocessing for missing values, categorical variables, and skewed numeric features.

Evaluate the model using threshold-free and threshold-based metrics.

Recommend a decision threshold for underwriting based on business tradeoffs.

Business Context

Dataset

The training data contains historical funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit attributes	9	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Loan attributes	7	loan_amount, interest_rate, term_months, purpose
Banking and income	6	annual_income, debt_to_income, monthly_obligations, verified_income
Behavioral / history	5	prior_loans, prior_defaults, days_since_last_loan, autopay_enrolled

Rows: 240K funded loans, 33 input features

Target: default_12m — whether the borrower becomes 90+ days delinquent within 12 months

Class balance: 11.5% default, 88.5% non-default

Missing data: ~8% missing in employment and income verification fields; ~3% missing in revolving utilization

Deliverables

Build a binary classification model to predict 12-month default.

Explain why the chosen algorithm fits this business problem.

Describe preprocessing for missing values, categorical variables, and skewed numeric features.

Evaluate the model using threshold-free and threshold-based metrics.

Recommend a decision threshold for underwriting based on business tradeoffs.