The Problem with Test Data in Financial Services
Every financial services firm building compliance tools, testing algorithms, or training advisors faces the same dilemma: real client data creates privacy and regulatory exposure, but hand-crafted test data is slow, inconsistent, and never covers the edge cases that matter most. WealthSynth solves this with a purpose-built corpus of synthetic households that are financially coherent, temporally deep, and legally safe to use anywhere.
Compliance & RegTech
Test Reg BI suitability engines, DOL fiduciary fee reasonableness tools, and AML/KYC workflows against realistic household scenarios without touching client data.
Algorithm Validation
Validate tax-loss harvesting algorithms, retirement income sequencing engines, and cash flow optimization models against 10,000 diverse household profiles.
Product Development
Build and demo financial planning features, onboarding workflows, and advisor tools using data that looks and behaves like real client portfolios.
31 Purpose-Built Data Packages
Same 10,000-household corpus. Different filters, annotations, and documentation for each use case.
Tier 1 — High Revenue, Low Deviation
— High Revenue, Low Deviation — broad coverage datasets with high buyer demandTier 2 — Moderate Revenue, Moderate Deviation
— Moderate Revenue, Moderate Deviation — specialist datasets for targeted use casesTier 3 — Niche Revenue, Higher Deviation
— Niche Revenue, Higher Deviation — specialized datasets for complex planning scenariosThe decision layer for your dataset
Who decided what for a client, why, what policy said, and how it turned out — with the regulatory figure behind each choice. Ready-made evaluation and demo data for AI assistant memory, matched to the client archetypes of each WealthSynth dataset. Sold and delivered by WealthSchema; each card links to the product page.
Every decision record in the DecisionSynth library — the full decision layer across all client archetypes: what was decided, why, what policy said, and what happened. The evaluation and seed-memory corpus for teams building or buying AI assistant memory, with zero PII.
Is your AI giving this year's advice — or last year's?
Tax-year-vintaged evaluation packs for AI financial-advice systems: every task keyed to a primary-source-verified figure, with the wrong-but-plausible prior-year values enumerated — so a scored, documented answer to "how often does our AI quote last year's limits" can sit in a compliance file. Sold and delivered by WealthSchema; each card links to the product page.
The complete evaluation library for AI financial-advice systems — figure accuracy, threshold behavior, and decision memory in one purchase, with documented held-out exclusions so results stay auditable.
A defensible answer to “how often is our AI advisor quoting last year's limits?” — a scored, documented evaluation your compliance file can cite, built on primary-source-verified 2026 figures.
Tests whether an AI system knows that crossing a Medicare income threshold by one dollar costs the full surcharge — and whether it's using this year's threshold or last year's.
Measures whether an AI assistant actually remembers what was decided for a client and why — scored against decision records whose ground truth is known by construction, not labeled after the fact.
DecisionSynth: Decision Memory for Agent Evaluation
From the team behind WealthSynth: synthetic decision episodes over the same household corpus — who decided what, under which cited regulatory figures, and why policy was overridden. DecisionSynth Bench is a free, open benchmark that measures whether an agent memory system can surface decision-relevant context, with ground truth known by construction.
Decision Memory Packs — episode add-ons for the household bundles above — are sold through WealthSchema, alongside the full decision corpus and custom decision batches.
Need the Full 10,000-Household Corpus?
Access the complete dataset, raw JSON schemas, and API access through WealthSchema — the developer platform built for teams who need the full depth.