Long-form how-to guides on building, validating, and shipping financial software with synthetic wealth data. Each guide cross-links to the Wealth Data Sets that exercise the patterns in production.
How to construct a synthetic-household test corpus that exercises every Reg BI Care Obligation scenario examiners actually cite — concentrated holdings, senior clients, cognitive markers, recent inheritances — without using any real client data.
A defensible methodology for backtesting TLH algorithms — lot-level cost basis, cross-account wash-sale conflicts, holding-period transitions, and the QSBS interactions most synthetic data fixtures skip entirely.
How to design a decumulation engine that integrates Social Security claiming, pension elections, RMDs, Roth conversions, and IRMAA tier management — and how to validate it against synthetic households spanning the full pre-retirement to late-retirement lifecycle.
A 6-week playbook for retiring production-data copies in dev, staging, and analytics in favor of synthetic data — with the parallel-validation, cutover, and rollback steps that prevent this from being a one-way migration.
How to stand up a fully synthetic, sales-ready demo environment that's realistic enough to close enterprise deals and architecturally guaranteed to never leak production data — including the per-prospect personalization pattern, account isolation, and reset cadence that prevent demo-environment misuse.
A four-stage stress-test playbook for production rebalancers — drift-band correctness, cross-account coordination, tax-aware execution, and adversarial market regimes — using a synthetic corpus calibrated for each test stage.
Step-by-step playbook for running production-realistic load tests against a wealth-tech API or service using a 10K-household synthetic corpus — concurrency profiles, hot-account simulation, hot-key avoidance, and the metrics the post-test review actually consumes.
A practitioner playbook for validating credit, underwriting, and pricing models against fair-lending requirements — adverse-action analysis, disparate-impact testing, and documenting the validation in a way that holds up to CFPB and OCC examination.
How to execute the three pillars of SR 11-7 model validation — conceptual soundness, ongoing monitoring, and outcomes analysis — using a synthetic-data corpus that gives the validation team independence from the development team and reproducibility examiners require.
How to take a synthetic-data corpus from procurement to first-customer-go-live in 30 days — the schema-mapping work, the integration tests, the security-review responses, and the customer-success handoff that determine whether the corpus delivers ongoing value.
How to run a quarterly compliance dry-run that exercises your supervisory engine, exception reporting, and audit-trail generation against a synthetic population — surfacing the program gaps that real exams reveal, on your timing and your dollar.