XGBoost vs DNN for Loan Default

Business Context

You’re on the Risk Modeling team at LendFlow, a fintech lender offering instant point-of-sale loans for e-commerce checkouts. The model’s prediction is used to (a) approve/decline applications and (b) set credit limits. LendFlow processes ~2.5M applications/month across the US and EU, and a 10 bps degradation in default rate translates to ~$4–6M/year in charge-offs. Regulators and internal audit require that decisions are explainable and that the model is monitored for drift and bias.

Your current baseline is a tuned logistic regression. Leadership wants you to evaluate whether a Deep Neural Network (DNN) can outperform XGBoost on this tabular dataset, and if so, whether the operational and compliance trade-offs are worth it.

Dataset

You have 12 months of historical applications with outcomes observed over a 90-day window.

Feature Group	Count	Examples	Notes
Applicant demographics	8	age_bucket, region, employment_status	Some features restricted in EU; must support feature gating
Credit bureau aggregates	22	tradelines_count, utilization_pct, delinquencies_12m	Strong predictors; missing for ~6% (thin-file)
Transaction & bank-link	18	avg_balance_30d, inflow_std_90d, nsf_events_90d	Missing for ~35% (user didn’t link bank)
Merchant & product	10	merchant_category, cart_amount, sku_risk_score	High-cardinality categorical
Device & fraud signals	12	device_age_days, ip_risk_score, velocity_1h	Noisy; distribution shifts during promos

Size: ~30M rows, 70 features (after basic cleaning)
Target: Binary — default_90d (1 if charged-off/90+ DPD within 90 days, else 0)
Class balance: Imbalanced — ~2.2% positive
Missing data: Structured missingness (bank-link missing not at random), plus sporadic nulls in bureau fields

Success Criteria

Risk performance: Improve ranking quality measured by AUC-PR and Recall at fixed FPR.
Operating point: At FPR = 3%, achieve Recall ≥ 55% on a held-out month.
Calibration: Predicted probabilities must be usable for limit setting; ECE ≤ 0.02 after calibration.
Latency: p95 < 40 ms per application for online scoring (model-only; features are precomputed).
Explainability: Provide reason codes for adverse action (top contributing features) and a defensible approach for audit.

Constraints

Temporal leakage risk: Outcomes occur after application; features must be strictly available at decision time.
Non-stationarity: Merchant mix and fraud signals drift during seasonal events.
Compute budget: Training must finish in < 6 hours on a single GPU box or a 32-core CPU machine.
Deployment: Model served in a Python microservice; prefer ONNX/TensorRT optional but not required.

Deliverables (what you must produce)

A clear recommendation: XGBoost vs DNN (or a hybrid), tied to the constraints above.
A training/evaluation plan that avoids leakage (time-based split) and handles imbalance.
Feature preprocessing strategy for both models (categoricals, missingness, scaling).
A thresholding and calibration approach to hit the operating point.
A production plan: inference architecture, monitoring, and retraining cadence.
A brief discussion of interpretability: SHAP vs integrated gradients vs surrogate models, and how you’d generate reason codes.

Problem

Business Context

Dataset

You have 12 months of historical applications with outcomes observed over a 90-day window.

Feature Group	Count	Examples	Notes
Applicant demographics	8	age_bucket, region, employment_status	Some features restricted in EU; must support feature gating
Credit bureau aggregates	22	tradelines_count, utilization_pct, delinquencies_12m	Strong predictors; missing for ~6% (thin-file)
Transaction & bank-link	18	avg_balance_30d, inflow_std_90d, nsf_events_90d	Missing for ~35% (user didn’t link bank)
Merchant & product	10	merchant_category, cart_amount, sku_risk_score	High-cardinality categorical
Device & fraud signals	12	device_age_days, ip_risk_score, velocity_1h	Noisy; distribution shifts during promos

Size: ~30M rows, 70 features (after basic cleaning)
Target: Binary — default_90d (1 if charged-off/90+ DPD within 90 days, else 0)
Class balance: Imbalanced — ~2.2% positive
Missing data: Structured missingness (bank-link missing not at random), plus sporadic nulls in bureau fields

Success Criteria

Risk performance: Improve ranking quality measured by AUC-PR and Recall at fixed FPR.
Operating point: At FPR = 3%, achieve Recall ≥ 55% on a held-out month.
Calibration: Predicted probabilities must be usable for limit setting; ECE ≤ 0.02 after calibration.
Latency: p95 < 40 ms per application for online scoring (model-only; features are precomputed).
Explainability: Provide reason codes for adverse action (top contributing features) and a defensible approach for audit.

Constraints

Temporal leakage risk: Outcomes occur after application; features must be strictly available at decision time.
Non-stationarity: Merchant mix and fraud signals drift during seasonal events.
Compute budget: Training must finish in < 6 hours on a single GPU box or a 32-core CPU machine.
Deployment: Model served in a Python microservice; prefer ONNX/TensorRT optional but not required.

Deliverables (what you must produce)

A clear recommendation: XGBoost vs DNN (or a hybrid), tied to the constraints above.
A training/evaluation plan that avoids leakage (time-based split) and handles imbalance.
Feature preprocessing strategy for both models (categoricals, missingness, scaling).
A thresholding and calibration approach to hit the operating point.
A production plan: inference architecture, monitoring, and retraining cadence.
A brief discussion of interpretability: SHAP vs integrated gradients vs surrogate models, and how you’d generate reason codes.

Problem

Business Context

Dataset

You have 12 months of historical applications with outcomes observed over a 90-day window.

Feature Group	Count	Examples	Notes
Applicant demographics	8	age_bucket, region, employment_status	Some features restricted in EU; must support feature gating
Credit bureau aggregates	22	tradelines_count, utilization_pct, delinquencies_12m	Strong predictors; missing for ~6% (thin-file)
Transaction & bank-link	18	avg_balance_30d, inflow_std_90d, nsf_events_90d	Missing for ~35% (user didn’t link bank)
Merchant & product	10	merchant_category, cart_amount, sku_risk_score	High-cardinality categorical
Device & fraud signals	12	device_age_days, ip_risk_score, velocity_1h	Noisy; distribution shifts during promos

Size: ~30M rows, 70 features (after basic cleaning)
Target: Binary — default_90d (1 if charged-off/90+ DPD within 90 days, else 0)
Class balance: Imbalanced — ~2.2% positive
Missing data: Structured missingness (bank-link missing not at random), plus sporadic nulls in bureau fields

Success Criteria

Risk performance: Improve ranking quality measured by AUC-PR and Recall at fixed FPR.
Operating point: At FPR = 3%, achieve Recall ≥ 55% on a held-out month.
Calibration: Predicted probabilities must be usable for limit setting; ECE ≤ 0.02 after calibration.
Latency: p95 < 40 ms per application for online scoring (model-only; features are precomputed).
Explainability: Provide reason codes for adverse action (top contributing features) and a defensible approach for audit.

Constraints

Temporal leakage risk: Outcomes occur after application; features must be strictly available at decision time.
Non-stationarity: Merchant mix and fraud signals drift during seasonal events.
Compute budget: Training must finish in < 6 hours on a single GPU box or a 32-core CPU machine.
Deployment: Model served in a Python microservice; prefer ONNX/TensorRT optional but not required.

Deliverables (what you must produce)

A clear recommendation: XGBoost vs DNN (or a hybrid), tied to the constraints above.
A training/evaluation plan that avoids leakage (time-based split) and handles imbalance.
Feature preprocessing strategy for both models (categoricals, missingness, scaling).
A thresholding and calibration approach to hit the operating point.
A production plan: inference architecture, monitoring, and retraining cadence.
A brief discussion of interpretability: SHAP vs integrated gradients vs surrogate models, and how you’d generate reason codes.

Problem

Business Context

Dataset

You have 12 months of historical applications with outcomes observed over a 90-day window.

Feature Group	Count	Examples	Notes
Applicant demographics	8	age_bucket, region, employment_status	Some features restricted in EU; must support feature gating
Credit bureau aggregates	22	tradelines_count, utilization_pct, delinquencies_12m	Strong predictors; missing for ~6% (thin-file)
Transaction & bank-link	18	avg_balance_30d, inflow_std_90d, nsf_events_90d	Missing for ~35% (user didn’t link bank)
Merchant & product	10	merchant_category, cart_amount, sku_risk_score	High-cardinality categorical
Device & fraud signals	12	device_age_days, ip_risk_score, velocity_1h	Noisy; distribution shifts during promos

Size: ~30M rows, 70 features (after basic cleaning)
Target: Binary — default_90d (1 if charged-off/90+ DPD within 90 days, else 0)
Class balance: Imbalanced — ~2.2% positive
Missing data: Structured missingness (bank-link missing not at random), plus sporadic nulls in bureau fields

Success Criteria

Risk performance: Improve ranking quality measured by AUC-PR and Recall at fixed FPR.
Operating point: At FPR = 3%, achieve Recall ≥ 55% on a held-out month.
Calibration: Predicted probabilities must be usable for limit setting; ECE ≤ 0.02 after calibration.
Latency: p95 < 40 ms per application for online scoring (model-only; features are precomputed).
Explainability: Provide reason codes for adverse action (top contributing features) and a defensible approach for audit.

Constraints

Temporal leakage risk: Outcomes occur after application; features must be strictly available at decision time.
Non-stationarity: Merchant mix and fraud signals drift during seasonal events.
Compute budget: Training must finish in < 6 hours on a single GPU box or a 32-core CPU machine.
Deployment: Model served in a Python microservice; prefer ONNX/TensorRT optional but not required.

Deliverables (what you must produce)

A clear recommendation: XGBoost vs DNN (or a hybrid), tied to the constraints above.
A training/evaluation plan that avoids leakage (time-based split) and handles imbalance.
Feature preprocessing strategy for both models (categoricals, missingness, scaling).
A thresholding and calibration approach to hit the operating point.
A production plan: inference architecture, monitoring, and retraining cadence.
A brief discussion of interpretability: SHAP vs integrated gradients vs surrogate models, and how you’d generate reason codes.

Interview Guides

Problem

Business Context

Dataset

Success Criteria

Constraints

Deliverables (what you must produce)

Problem

Business Context

Dataset

Success Criteria

Constraints

Deliverables (what you must produce)

XGBoost vs DNN for Loan Default

Problem

Business Context

Dataset

Success Criteria

Constraints

Deliverables (what you must produce)

Problem

Business Context

Dataset

Success Criteria

Constraints

Deliverables (what you must produce)