Life transitions are where financial advice matters most — and where the data tells you the modal client doesn't get it. Divorce, widowhood, sudden wealth, medical crisis, sandwich-generation caregiving, blended-family formation: each is a window where good planning compounds for decades, and where the absence of planning ossifies suboptimal structures into permanence. The Life Transitions Pack is 220 households mid-transition, built for the platforms that are trying to recognize the windows in real time and act on them.
Life-event detection is one of the highest-value features a wealth platform can build, and one of the hardest to test. The signal of an impending transition is rarely a single event; it's a cluster of smaller changes that, taken together, indicate the household is in or approaching a major life event. A new beneficiary on a brokerage account combined with a change in marital status combined with relocation activity might be a divorce. The same beneficiary change combined with hospital-related transactions might be a recent death. Detecting these correctly requires test data with the realistic event-density and event-clustering that real transitions produce.
Most fixture data is point-in-time and event-poor. A household record might note 'married' or 'divorced' but won't capture the cluster of behavioral and account changes that surround the transition. This makes it almost impossible to validate a life-event detection algorithm against realistic patterns — you can train on what 'divorced' looks like, but not on what 'in the middle of a contested divorce' looks like.
This Data Set is event-dense by design. The 220 households are all mid-transition, with structured event logs capturing the cluster of changes that surround each transition: account retitling, beneficiary updates, behavioral signals (anxiety scoring, spending pattern changes), asset reshuffling, and the documented life-event timeline. The corpus is the canonical fixture for life-event detection, post-transition planning, and behavioral nudge research.
Trains and validates the platform's life-event detection logic against 220 households whose transitions are explicitly labeled, with the structured event clusters surrounding each transition that real-world detection algorithms need to identify.
Tests the platform's outreach trigger logic — when to prompt the advisor to reach out, when to surface a beneficiary-update reminder, when to suggest a new-trust conversation — against realistic transitions where the timing of outreach materially affects client experience.
Tests the firm's divorce-planning workflow against realistic in-progress divorce households, ensuring the firm's intake and asset-retitling tooling captures the full structural complexity (separate property tracing, QDRO requirements, beneficiary updates, custody-related insurance changes).
Studies the behavioral patterns surrounding major life transitions using a corpus where the behavioral signals (anxiety scoring, spending pattern changes, advice-seeking behaviour) are structured alongside the transition events themselves.
Validates the firm's process for handling clients in life transition — ensuring beneficiary updates are completed, KYC is refreshed, suitability is reassessed, and supervisory review captures the heightened attention these clients warrant.
The 220 households span fourteen archetypes covering the full transition taxonomy: divorces in progress (S-01), post-bankruptcy recovery (S-02), medical-debt crisis (S-03), sandwich-generation caregivers (S-04), recent widowhood (RL-02, H-04), sudden wealth (P-06), distressed mortgages with potential modification (MB-02), blended-family formation (BL-01), and the behavioral profiles where transitions interact with planning failure (B-01 financial anxiety, B-03 lifestyle inflation).
Every household carries a dense behavioral event log: typically 8-15 events in the trailing 24 months, structurally tagged by event type. Transition events (divorce_filing, death_of_spouse, inheritance_received, medical_diagnosis, job_loss, remarriage, mortgage_modification) are explicitly typed. Surrounding events that cluster near transitions are also captured: account_retitled, beneficiary_updated, address_changed, advisor_outreach_initiated, behavioral_anxiety_spike. The financial stress score (1-100) is computed for each month of the longitudinal series so the trajectory through the transition is visible. Asset retitling activity is tracked: which accounts changed title, when, and what the new structure is. Pre/post-transition snapshots let your engine compare the household's structural state at two reference points.
The Data Set ships as JSON, CSV, and Parquet. The WealthSynth Methodology PDF documents the transition taxonomy, the event-clustering methodology (calibrated against the Holmes-Rahe stress-event scale and the CFP Board's life-event-driven planning taxonomy), the financial-stress-scoring methodology, and the asset-retitling tracking structure.
A redacted summary of one household from this Data Set — names, employers, exact balances, and metro area are stripped. Ages are bucketed, income and net worth are reported as bands. The full record (and all 220 like it) ships in the ZIP.
{
"events.life_events[]": <value>,
"events.transition_type": <value>,
"behavioral.financial_stress_score": <value>,
"estate.retitling_required": <value>,
"advisor.transition_planning_status": <value>
}Returns households whose financial stress score has spiked in the last 90 days AND whose advisor has not yet been contacted post-spike — the highest-priority outreach queue for proactive advisor engagement.
households.filter(h => {
const recentMonths = h.longitudinal.monthly.slice(-3);
const earlierMonths = h.longitudinal.monthly.slice(-9, -3);
const recentMax = Math.max(...recentMonths
.map(m => m.behavioral_stress_score));
const earlierMax = Math.max(...earlierMonths
.map(m => m.behavioral_stress_score));
const recentOutreach = h.events.life_events
.find(e => e.type === 'advisor_outreach_initiated' &&
monthsSince(e.date) < 3);
return recentMax - earlierMax > 20 && !recentOutreach;
})Returns households whose recent transition event (divorce, remarriage, death of spouse) suggests beneficiary updates are needed AND whose beneficiary designations haven't been updated since the transition.
households.filter(h => {
const transition = h.events.life_events.find(e =>
['divorce_finalized', 'remarriage', 'death_of_spouse']
.includes(e.type) && monthsSince(e.date) < 12);
if (!transition) return false;
const lastBeneficiaryUpdate = Math.max(
...h.estate.beneficiaries_by_account
.map(b => b.last_updated_month));
return lastBeneficiaryUpdate < transition.month;
})Returns households formed through remarriage with at least one spouse bringing children from a prior marriage — the planning population for QTIP-style balancing structures.
households.filter(h => h.events.life_events.some(e => e.type === 'remarriage') && h.demographics.has_step_children )
For each transition event in the past 12 months, returns the count of accounts that have been retitled vs. the count that still need retitling — the operations queue for completing the post-transition asset-restructuring work.
households.flatMap(h =>
h.events.life_events.filter(e =>
e.transition_required_retitling &&
monthsSince(e.date) < 12
).map(e => ({
household: h.id,
transition: e.type,
retitled_count: h.events.retitling_events.filter(r =>
r.triggering_event === e.id && r.completed).length,
pending_count: h.events.retitling_events.filter(r =>
r.triggering_event === e.id && !r.completed).length
}))
)Each household's transition event cluster is generated against archetype-specific patterns. Divorce-in-progress households have realistic event sequences: filing date, separation date, asset-discovery period, settlement-negotiation period, finalization event. Recent-widowhood households have realistic post-mortem sequences: death event, account-access-establishment period, beneficiary-distribution events, and the surviving-spouse-planning period that follows. Sudden-wealth households have realistic post-event sequences: receipt event, decision-paralysis period (where research shows most lottery winners and inheritance recipients sit on uninvested funds for 6-12 months), advisor-engagement event, and the planning-implementation period. The event clustering is calibrated against industry research on transition timelines (the Sudden Money Institute's research, the CFP Board's life-event taxonomy, and academic research on divorce financial outcomes). The corpus passes the WealthSynth consistency validator (event sequences are internally consistent; account-retitling activity reconciles with the underlying transition; financial-stress score trajectories are mathematically computed from the underlying financial data) and the LLM-as-judge gate. Annual refresh tracks any updates to the transition-research literature.
Yes. Divorce-in-progress households have realistic asset-division structures: separate property identification, marital property identification, QDRO requirements for retirement accounts, alimony / child-support obligations where applicable, and the realistic timeline (typically 12-18 months from filing to finalization). The Sudden Money Institute's divorce-planning framework is referenced in the Methodology PDF.
The 1-100 score combines four factors: liquidity stress (months of expenses available), debt stress (DTI ratio), behavioral stress (account-access frequency, advice-seeking activity), and event stress (Holmes-Rahe-weighted recent life events). The score is computed monthly across the longitudinal series so the trajectory through a transition is visible. The Methodology PDF documents the weighting and the calibration sources.
Yes. About 18% of the corpus is sandwich-generation households (S-04) — typically 50-65 year olds caring for an aging parent while still supporting children or grandchildren. The structured caregiver-burden data tracks the financial cost of caregiving (out-of-pocket medical, lost income, caregiving-related expenses) and the planning impact (delayed retirement, reduced savings, increased anxiety scoring).
About 12% of the corpus is blended families (BL-01 archetype). The structured data captures both spouses' children from prior marriages, the financial obligations to ex-spouses (alimony, child support, education funding commitments), and the planning structures appropriate for blended families (typically QTIP elections for marital deduction with remainder to deceased spouse's children).
Yes. About 25% of the corpus exhibits elevated behavioral indicators (B-01 financial anxiety profiles), with structured anxiety scoring, account-checking-frequency data, and advice-avoidance markers. These households are particularly important for behavioral-finance research and for the platforms building behavioral-nudge tools.
Yes. About 8% of the corpus is sudden-wealth recipients (P-06) — typically inheritance, business sale proceeds, or major equity vesting. The structured post-event sequence reflects the realistic decision-paralysis period (6-12 months of uninvested cash before structural decisions are made), the realistic advisor-engagement timeline, and the realistic post-investment behavioral patterns.
Yes — that's a primary use case. The structured event log lets your CRM-integration tooling consume transition events and trigger appropriate outreach. The event-clustering data shows the time windows where outreach has highest expected value (typically the first 30-90 days post-transition).
B11 focuses on planning readiness scoring for HNW estate planning. B30 focuses on behavioral patterns that affect financial outcomes regardless of life-stage. B27 focuses specifically on transitions — the high-leverage windows where intervention compounds. Many tech buyers purchase B27 + B30 together for the integrated transition-detection-plus-behavioral-coaching capability.
130 households with elevated behavioral risk: financial-anxiety avoiders, overconfident DIY investors with concentration, lifestyle-inflation spenders, post-divorce rebuilders, and student-loan tax-bomb candidates. Each carries a behavioral profile and intervention-trigger flags.
110 HNW and UHNW households with estate planning readiness scores, trust structures, gifting histories, charitable giving data, and GST exemption tracking. Complements B09 (Next-Gen Attrition) and B12 (Estate & Trust Planning).
200 households across the wealth-transfer lifecycle: young families establishing wills, dual-income couples buying first homes, peak earners building trusts, retirees coordinating beneficiaries, and blended families with QTIP planning. Each carries a current estate document inventory.
Purchases are for internal use only. Redistribution or resale of data is prohibited under the WealthSchema Data License.
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