RESEARCH BY INDUSTRY
Find published research and synthetic demonstrations, with planned and developing topics clearly separated.
9 published public-data studies4 synthetic demonstrations51 planned or developing topics
1 published study · 2 planned or developing topics
S28 / Banking and fintech
A sales operations manager has a limited outbound-contact budget. Determine whether calibrated subscription-response models improve the concentration of successful contacts relative to simple recency and prior-contact rules.
Evaluated public-data study
Read the finding and evidence ↗Calibrated classification; additive models; expected-value ranking
These topics are separate from completed findings. Each status reflects the work actually performed.
S29 / Banking and fintech
A lending strategy and model-risk team wants to understand geographic variation in application outcomes and which differences remain after measured application characteristics are considered.
Planned topic
Multilevel models; standardization; selection-bias sensitivity
Create comparable HMDA cohorts by year, loan purpose, product and application disposition. Keep withdrawals and incomplete files distinct from denials. Use multilevel logistic models and standardized outcome comparisons; audit privacy-modified, censored and missing fields before constructing covariates.
Evaluation: Hold out later years and selected lenders/regions. Compare raw versus standardized differences with uncertainty, calibration and sensitivity to covariate sets. Avoid ranking tiny cells; evaluate missingness patterns and changes in reporting definitions.
Boundary: HMDA is not a loan-performance panel and does not contain every underwriting factor. This is an audit and research demonstration, not a legal finding of discrimination or a deployable credit-decision engine.
S30 / Banking and fintech
A risk analytics leader must understand how probability errors affect a hypothetical review policy. Compare interpretable and flexible default models under asymmetric error costs and limited manual-review capacity.
Planned topic
Proper scoring rules; probability calibration; expected loss
Use the available repayment, bill and payment history to predict the provided next-month default outcome. Fit logistic and monotone/additive baselines plus boosted trees. Reserve an untouched customer-level test set, calibrate on separate validation data and document feature timing.
Evaluation: Use nested development folds and the fixed test set; do not invent a temporal holdout absent multiple cohorts. Report Brier score, log loss, calibration by risk band, PR performance and decision-cost sensitivity. Analyze subgroup errors without making automatic individual lending decisions.
Boundary: This is an older Taiwan cohort with no prospective U.S. validation. Default prediction is not fraud detection; assumed exposure and losses must be separated from observed labels and no actual lending policy is validated.
The 60-topic program spans 20 industries, with 9 studies published. Synthetic demonstrations are counted separately. Unpublished topics have no public case-study route.
