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
S31 / Insurance
An insurance analytics team needs accurate expected-loss estimates that remain understandable across policy segments. Test whether a nonlinear model improves frequency-severity predictions while preserving portfolio and segment calibration.
Evaluated public-data study
Read the finding and evidence ↗Exposure-offset GLMs; frequency-severity; Tweedie models
These topics are separate from completed findings. Each status reflects the work actually performed.
S32 / Insurance
An insurer or resilience planner needs to understand tail payments and correlated geographic exposure. Estimate how claim-severity conclusions change when major flood events, coverage limits and portfolio composition are considered.
Planned topic
Hierarchical severity; extreme-value analysis; event-level dependence
Clean NFIP claim payments and dated event/geography information. Analyze severity conditional on a reported claim using hierarchical models and carefully diagnosed tail models. For incidence or expected loss per insured exposure, explicitly add compatible redacted policy data and define earned-exposure denominators first.
Evaluation: Hold out entire major events or event-years and geographic groups. Compare empirical severity and lognormal/Gamma baselines with tail models. Report tail quantile calibration, threshold sensitivity and event-bootstrap uncertainty; flag unsupported extrapolation beyond observed experience.
Boundary: Claims alone cannot establish flood probability or claim incidence. Redacted geography, evolving limits and nominal payment amounts complicate comparisons; modeled return periods or portfolio losses need explicit additional assumptions.
S33 / Insurance
A benefits or insurance planning team must understand how mortality assumptions affect a long-duration payment obligation. Quantify sensitivity to table choice, longevity improvement and discount rates without pretending the tables are individual training records.
Planned topic
Life-contingent valuation; survival probabilities; sensitivity analysis
Select compatible SOA tables with clear population, vintage and select/ultimate or generational definitions. Convert death probabilities into survival curves and expected payment streams. Apply a clearly synthetic cohort of ages and benefits, then compare deterministic and explicitly assumed stochastic improvement scenarios.
Evaluation: Check survival monotonicity, probability identities and actuarial present values against hand-calculated cases. Compare suitable table vintages and run rate/improvement stress grids. Do not report predictive accuracy without separate death and exposure observations.
Boundary: This is a transparent scenario engine using published rates and synthetic obligations. It is not a fitted individual mortality model, an audited reserve calculation or a validated forecast of future longevity.
The 60-topic program spans 20 industries, with 9 studies published. Synthetic demonstrations are counted separately. Unpublished topics have no public case-study route.
