Build a Loan Default Classifier | Dataford Interview Questions

Business Context

LendWise, a digital consumer lending platform processing ~250K applications per quarter, wants a model to predict whether an approved borrower will default within 90 days of origination. The goal is to improve underwriting decisions while keeping the model explainable for risk and compliance teams.

Dataset

Feature Group	Count	Examples
Applicant demographics	6	age, employment_status, region, housing_status
Credit bureau variables	11	credit_score, delinquencies_12m, total_open_accounts, utilization_rate
Application attributes	8	loan_amount, term_months, interest_rate, purpose
Bank transaction aggregates	9	avg_monthly_income, income_volatility, nsf_count_90d, avg_balance
Behavioral / derived	6	application_hour, device_risk_score, prior_applications_30d, fraud_flag_history

Rows: 320K historical loans, 40 features
Target: default_90d — 1 if borrower misses payments and is charged off within 90 days, else 0
Class balance: 11.6% positive, 88.4% negative
Missing data: 18% missing in bank transaction features, 6% missing in bureau variables for thin-file applicants

Success Criteria

A solution is considered good enough if it achieves ROC-AUC >= 0.82, PR-AUC >= 0.42, and recall >= 0.70 at precision >= 0.35 on a held-out test set. The candidate should also show a clear, repeatable model-building process from raw data to threshold selection.

Constraints

Predictions must be returned in <50 ms for online underwriting.
The model must support reason codes / feature importance for adverse action review.
Retraining is expected monthly using the latest approved-loan data.
Avoid leakage from post-origination variables.

Deliverables

Define a general model-building process for this classification task.
Build and compare at least one baseline and one stronger model.
Explain preprocessing, feature engineering, validation, and leakage checks.
Select evaluation metrics and a decision threshold aligned to business risk.
Describe how the model would be deployed and monitored in production.

Problem

Business Context

Dataset

Feature Group	Count	Examples
Applicant demographics	6	age, employment_status, region, housing_status
Credit bureau variables	11	credit_score, delinquencies_12m, total_open_accounts, utilization_rate
Application attributes	8	loan_amount, term_months, interest_rate, purpose
Bank transaction aggregates	9	avg_monthly_income, income_volatility, nsf_count_90d, avg_balance
Behavioral / derived	6	application_hour, device_risk_score, prior_applications_30d, fraud_flag_history

Rows: 320K historical loans, 40 features
Target: default_90d — 1 if borrower misses payments and is charged off within 90 days, else 0
Class balance: 11.6% positive, 88.4% negative
Missing data: 18% missing in bank transaction features, 6% missing in bureau variables for thin-file applicants

Success Criteria

Constraints

Predictions must be returned in <50 ms for online underwriting.
The model must support reason codes / feature importance for adverse action review.
Retraining is expected monthly using the latest approved-loan data.
Avoid leakage from post-origination variables.

Deliverables

Define a general model-building process for this classification task.
Build and compare at least one baseline and one stronger model.
Explain preprocessing, feature engineering, validation, and leakage checks.
Select evaluation metrics and a decision threshold aligned to business risk.
Describe how the model would be deployed and monitored in production.

Problem

Business Context

Dataset

Feature Group	Count	Examples
Applicant demographics	6	age, employment_status, region, housing_status
Credit bureau variables	11	credit_score, delinquencies_12m, total_open_accounts, utilization_rate
Application attributes	8	loan_amount, term_months, interest_rate, purpose
Bank transaction aggregates	9	avg_monthly_income, income_volatility, nsf_count_90d, avg_balance
Behavioral / derived	6	application_hour, device_risk_score, prior_applications_30d, fraud_flag_history

Rows: 320K historical loans, 40 features
Target: default_90d — 1 if borrower misses payments and is charged off within 90 days, else 0
Class balance: 11.6% positive, 88.4% negative
Missing data: 18% missing in bank transaction features, 6% missing in bureau variables for thin-file applicants

Success Criteria

Constraints

Predictions must be returned in <50 ms for online underwriting.
The model must support reason codes / feature importance for adverse action review.
Retraining is expected monthly using the latest approved-loan data.
Avoid leakage from post-origination variables.

Deliverables

Define a general model-building process for this classification task.
Build and compare at least one baseline and one stronger model.
Explain preprocessing, feature engineering, validation, and leakage checks.
Select evaluation metrics and a decision threshold aligned to business risk.
Describe how the model would be deployed and monitored in production.

Problem

Business Context

Dataset

Feature Group	Count	Examples
Applicant demographics	6	age, employment_status, region, housing_status
Credit bureau variables	11	credit_score, delinquencies_12m, total_open_accounts, utilization_rate
Application attributes	8	loan_amount, term_months, interest_rate, purpose
Bank transaction aggregates	9	avg_monthly_income, income_volatility, nsf_count_90d, avg_balance
Behavioral / derived	6	application_hour, device_risk_score, prior_applications_30d, fraud_flag_history

Rows: 320K historical loans, 40 features
Target: default_90d — 1 if borrower misses payments and is charged off within 90 days, else 0
Class balance: 11.6% positive, 88.4% negative
Missing data: 18% missing in bank transaction features, 6% missing in bureau variables for thin-file applicants

Success Criteria

Constraints

Predictions must be returned in <50 ms for online underwriting.
The model must support reason codes / feature importance for adverse action review.
Retraining is expected monthly using the latest approved-loan data.
Avoid leakage from post-origination variables.

Deliverables

Define a general model-building process for this classification task.
Build and compare at least one baseline and one stronger model.
Explain preprocessing, feature engineering, validation, and leakage checks.
Select evaluation metrics and a decision threshold aligned to business risk.
Describe how the model would be deployed and monitored in production.