In-depth guides to the planning themes that shape every Wealth Data Set: tax-loss harvesting, equity comp, retirement income sequencing, ESG alignment, and more.
What realistic test data looks like for fintechs building on Plaid, Yodlee, Akoya, MX, or direct custodian feeds. The reconciliation problem, the ACATS edge cases, the custodian-specific quirks, and the schemas that exercise them.
What synthetic test data has to look like for platforms serving expats, inbound foreign nationals, and multi-currency portfolios. PFIC tracking, foreign tax credits, treaty-tier withholding, FBAR / FATCA reporting, and the worldwide-income complications most domestic-only platforms underhandle.
What synthetic test data has to look like for the decumulation product surface most retirement platforms underhandle — fixed and variable annuities, HSA investment tracking, non-qualified deferred compensation, defined-benefit pension modeling, and the lump-sum-vs-annuity decision.
ISOs, NSOs, RSUs, ESPPs, and the AMT cliff — what an equity-comp planning engine actually needs in its data model, and why naive grant-level data falls apart at exercise.
Why the order of withdrawals matters more than the size of the portfolio — and what your retirement-income engine has to model to get the math right.
How tax-loss harvesting actually works in production fintech systems — lot accounting, wash-sale tracking, QSBS interactions, and the data shape your engine needs.
Why mock-data tools systematically break on time series — corporate actions, returns generation, performance attribution, survivorship bias — and what your synthetic test data has to do instead.