Predict Loan Default for Fintech | Dataford Interview Questions

Business Context

LendWise, a consumer lending fintech processing roughly 120K loan applications per month, wants a machine learning model to predict whether an applicant will default within 12 months of loan origination. The model will support underwriting decisions and risk-based pricing, so it must improve risk separation without creating an overly complex production system.

Dataset

You are given a historical dataset of funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit bureau variables	12	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Financial variables	10	annual_income, debt_to_income, existing_loans, monthly_obligations
Loan attributes	7	loan_amount, term_months, interest_rate, purpose
Behavioral / derived	5	income_to_loan_ratio, credit_age_months, recent_inquiry_rate

Rows: 420K funded loans, 40 features
Target: default_12m — whether the customer becomes 90+ days past due or charged off within 12 months
Class balance: 11.6% default, 88.4% non-default
Missing data: 18% missing in employment_length, 9% in annual_income, and light missingness (<3%) in several bureau fields

Success Criteria

A good solution should achieve meaningful lift over a logistic regression baseline, with strong ranking quality for underwriting. Target performance is ROC-AUC >= 0.82, PR-AUC >= 0.42, and a top-decile lift > 2.5 on a held-out test set.

Constraints

Predictions must be generated in <50 ms per application in an online API.
Risk and compliance teams require feature-level explanations.
The model should be retrained monthly and remain stable under moderate feature drift.

Deliverables

Build a binary classification pipeline to predict 12-month default.
Explain model choice, preprocessing, and feature engineering decisions.
Define a validation strategy that avoids leakage from future-originated loans.
Report evaluation metrics and recommend an operating threshold for underwriting.
Provide production-ready Python code for training and inference.

Problem

Business Context

Dataset

You are given a historical dataset of funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit bureau variables	12	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Financial variables	10	annual_income, debt_to_income, existing_loans, monthly_obligations
Loan attributes	7	loan_amount, term_months, interest_rate, purpose
Behavioral / derived	5	income_to_loan_ratio, credit_age_months, recent_inquiry_rate

Rows: 420K funded loans, 40 features
Target: default_12m — whether the customer becomes 90+ days past due or charged off within 12 months
Class balance: 11.6% default, 88.4% non-default
Missing data: 18% missing in employment_length, 9% in annual_income, and light missingness (<3%) in several bureau fields

Success Criteria

Constraints

Predictions must be generated in <50 ms per application in an online API.
Risk and compliance teams require feature-level explanations.
The model should be retrained monthly and remain stable under moderate feature drift.

Deliverables

Build a binary classification pipeline to predict 12-month default.
Explain model choice, preprocessing, and feature engineering decisions.
Define a validation strategy that avoids leakage from future-originated loans.
Report evaluation metrics and recommend an operating threshold for underwriting.
Provide production-ready Python code for training and inference.

Problem

Business Context

Dataset

You are given a historical dataset of funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit bureau variables	12	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Financial variables	10	annual_income, debt_to_income, existing_loans, monthly_obligations
Loan attributes	7	loan_amount, term_months, interest_rate, purpose
Behavioral / derived	5	income_to_loan_ratio, credit_age_months, recent_inquiry_rate

Rows: 420K funded loans, 40 features
Target: default_12m — whether the customer becomes 90+ days past due or charged off within 12 months
Class balance: 11.6% default, 88.4% non-default
Missing data: 18% missing in employment_length, 9% in annual_income, and light missingness (<3%) in several bureau fields

Success Criteria

Constraints

Predictions must be generated in <50 ms per application in an online API.
Risk and compliance teams require feature-level explanations.
The model should be retrained monthly and remain stable under moderate feature drift.

Deliverables

Build a binary classification pipeline to predict 12-month default.
Explain model choice, preprocessing, and feature engineering decisions.
Define a validation strategy that avoids leakage from future-originated loans.
Report evaluation metrics and recommend an operating threshold for underwriting.
Provide production-ready Python code for training and inference.

Problem

Business Context

Dataset

You are given a historical dataset of funded loans from the last 3 years.

Feature Group	Count	Examples
Applicant demographics	6	age, employment_length, home_ownership, state
Credit bureau variables	12	fico_score, revolving_utilization, delinquencies_2y, inquiries_6m
Financial variables	10	annual_income, debt_to_income, existing_loans, monthly_obligations
Loan attributes	7	loan_amount, term_months, interest_rate, purpose
Behavioral / derived	5	income_to_loan_ratio, credit_age_months, recent_inquiry_rate

Rows: 420K funded loans, 40 features
Target: default_12m — whether the customer becomes 90+ days past due or charged off within 12 months
Class balance: 11.6% default, 88.4% non-default
Missing data: 18% missing in employment_length, 9% in annual_income, and light missingness (<3%) in several bureau fields

Success Criteria

Constraints

Predictions must be generated in <50 ms per application in an online API.
Risk and compliance teams require feature-level explanations.
The model should be retrained monthly and remain stable under moderate feature drift.

Deliverables

Build a binary classification pipeline to predict 12-month default.
Explain model choice, preprocessing, and feature engineering decisions.
Define a validation strategy that avoids leakage from future-originated loans.
Report evaluation metrics and recommend an operating threshold for underwriting.
Provide production-ready Python code for training and inference.