Behavior is the largest driver of long-run financial outcomes — and the hardest variable to model. The literature is clear that household financial outcomes correlate more strongly with behavioral consistency (savings discipline, portfolio rebalancing through stress, avoidance of behavioral mistakes at extremes) than with any single financial metric. The wealth platforms that integrate behavioral coaching into their workflow consistently outperform on client retention and satisfaction. The Behavioral Finance Coaching Pack is 130 households deliberately structured to surface the behavioral patterns that drive intervention opportunities.
Building behavioral-coaching tools requires structural data that captures the patterns behavioral coaching addresses: financial anxiety, overconfidence with concentration, lifestyle inflation, post-loss avoidance, post-divorce rebuilding, and the trigger-event windows where coaching has the highest leverage. Most fixture data captures the financial side cleanly but the behavioral side poorly — a household record might note 'spending high' but won't capture the underlying anxiety profile, the trigger-event history, or the intervention-response patterns that distinguish a transient pattern from a structural one.
For builders, this means behavioral-coaching features get developed against intuition rather than against representative data. The recommendation engine that surfaces 'this client should be nudged toward savings' is calibrated against a cartoon of behavioral patterns rather than the structurally diverse cases the engine will encounter in production.
This Data Set provides 130 households where the behavioral structure is fully expressed: bias profiles across the canonical 8 behavioral-finance dimensions (loss aversion, present bias, overconfidence, anchoring, herd behaviour, mental accounting, status quo bias, framing), anxiety scoring with longitudinal trajectory, intervention-trigger flags that fire when underlying conditions warrant outreach, and the coaching engagement history showing how the household has responded to past interventions.
Validates the platform's nudge-trigger logic, intervention-recommendation engine, and coaching-engagement scoring against 130 households whose behavioural profiles span the realistic range from low-anxiety / disciplined to high-anxiety / avoidant.
Tests the coaching-platform's curriculum-recommendation logic, engagement-tracking, and behavioural-progress measurement against client profiles whose coaching journey is structurally documented — pre-coaching baseline, intervention response patterns, post-intervention trajectory.
Tests the firm's concentration-risk intervention workflow against overconfident DIY investors with significant single-stock concentration. Validates that the firm's outreach script and the recommended diversification path are appropriately calibrated to the client's concentration profile and behavioural pattern.
Validates the platform's spending-trigger detection logic against lifestyle-inflation patterns, post-divorce spending changes, and post-loss avoidance patterns. Surfaces the cases where naive spending alerts miss the underlying behavioural driver.
Studies behavioural patterns in financial decision-making using a corpus where the bias profiles, life-event timelines, and behavioural trajectories are structurally documented — supporting research on intervention efficacy and behaviour-change persistence.
The 130 households cluster around behavioural-risk archetypes: F-04 first-generation wealth builders (where building habits are still being established); S-01 divorce in progress (where behavioural stress is highest); S-02 post-bankruptcy recovery (where rebuilding behaviours matter); S-03 medical-debt crisis (where avoidance patterns concentrate); B-01 financial anxiety / avoiders; B-02 overconfident DIY investors with concentration; B-03 lifestyle inflation / spenders; MB-02 distressed homeowners; SL-02 IDR-enrolled student-loan borrowers (where forgiveness-tax-bomb anxiety concentrates).
Every household has a structured behavioural profile: bias scores across the canonical 8 behavioral-finance dimensions on a 0-100 scale; anxiety score with the trailing 12-month trajectory; intervention-trigger flags (loss-aversion-driven sell flag, FOMO-driven concentration flag, lifestyle-inflation flag, avoidance flag, recency-bias-driven crypto-allocation flag); coaching engagement history with the structured response patterns to past interventions; spending discretionary percentage with the trailing 12-month trajectory. Where behavioural patterns interact with life events (divorce, bankruptcy, medical crisis), the structured event log captures the timing.
The Data Set ships as JSON and CSV. The WealthSynth Methodology PDF documents the bias-profile taxonomy, the anxiety-scoring methodology, the intervention-trigger framework, the coaching-engagement scoring approach, and the calibration source for typical behavioural profiles by archetype.
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 130 like it) ships in the ZIP.
{
"behavioral.bias_profile": <value>,
"behavioral.anxiety_score": <value>,
"behavioral.intervention_triggers": <value>,
"behavioral.coaching_engagement": <value>,
"spending.discretionary_pct": <value>
}Returns overconfident DIY investors whose single-stock concentration exceeds 25% of liquid assets — the highest-leverage outreach queue for concentration-risk intervention.
households.filter(h => h.behavioral.bias_profile.overconfidence > 70 && h.assets.concentration_pct > 0.25 )
Returns households whose discretionary-spending percentage has increased by more than 10 percentage points over the past 12 months — the queue for spending-trigger outreach.
households.filter(h => {
const trailing12 = h.spending.discretionary_pct_history.slice(-12);
const prior12 = h.spending.discretionary_pct_history.slice(-24, -12);
return mean(trailing12) - mean(prior12) > 0.10;
})Returns households whose recent advisor-touchpoint frequency has declined AND whose stated financial-anxiety score is high — the avoidance-pattern signature where outreach is most needed.
households.filter(h => {
const recentTouchpoints = h.engagement.touchpoint_history
.filter(t => monthsSince(t.date) <= 6).length;
const earlierTouchpoints = h.engagement.touchpoint_history
.filter(t => monthsSince(t.date) > 6 &&
monthsSince(t.date) <= 12).length;
return recentTouchpoints < earlierTouchpoints * 0.5 &&
h.behavioral.anxiety_score > 70;
})For households with past coaching interventions, returns the structured response pattern — engagement-rate change, behaviour-pattern shift, anxiety-score trajectory — useful for analyzing intervention efficacy.
households.filter(h =>
h.behavioral.coaching_history?.intervention_count > 0
).map(h => ({
id: h.id,
intervention_type: h.behavioral.coaching_history.dominant_type,
pre_intervention_anxiety: h.behavioral.coaching_history
.pre_intervention_anxiety,
post_intervention_anxiety: h.behavioral.coaching_history
.post_intervention_anxiety,
behavior_change_score: h.behavioral.coaching_history
.behavior_change_score
}))Each household's behavioural profile is generated against archetype-specific patterns. Financial-anxiety avoiders (B-01) have realistic patterns of low advisor-touchpoint frequency, elevated avoidance markers, and conservative asset allocation. Overconfident DIY investors (B-02) have realistic concentration patterns and elevated trading frequency. Lifestyle-inflation spenders (B-03) have realistic discretionary-spending trajectories. The 8-dimension bias profile is calibrated against published behavioural-finance research (Kahneman, Thaler, Statman). Anxiety scoring uses a longitudinal series so the trajectory through life events is visible. Intervention-trigger flags fire based on structurally observable conditions; the corpus calibrates the flag-firing rate against industry data on advisor-outreach trigger volume. Coaching-engagement history reflects realistic distributions — most households have low engagement; a smaller share has highly active engagement with structurally documented response patterns. The corpus passes the WealthSynth consistency validator (bias profiles are coherent with archetype; anxiety trajectories are consistent with life events; intervention-trigger flags fire when underlying conditions are met) and the LLM-as-judge gate. Annual refresh tracks any updates to the behavioural-finance research literature.
The 8-dimension profile (loss aversion, present bias, overconfidence, anchoring, herd behaviour, mental accounting, status quo bias, framing) is calibrated against the Kahneman / Thaler / Statman behavioural-finance research literature. The 0-100 scoring on each dimension reflects the relative-strength of the bias for the household — a 70+ score indicates the bias is meaningfully influencing decisions; a 30- score indicates the bias is well-managed.
The anxiety scoring is calibrated against research on financial anxiety as a behavioural construct (research by Klontz, Britt, and colleagues on financial-anxiety scales). The scoring is not a clinical diagnosis — it's a structural proxy for the financial-decision-making impact that anxiety produces. The Methodology PDF documents the calibration sources.
The corpus has structured intervention-trigger flags pre-populated based on the underlying household conditions. For platforms wanting different trigger logic, the structured underlying data (concentration percentage, anxiety trajectory, life events) lets the platform compute its own triggers without re-generating the corpus.
Yes. Coaching engagement and efficacy data is calibrated against published research on financial-coaching outcomes (CFP Board research, Sherraden and colleagues on financial-capability research). About 25% of the corpus's coaching-engaged households show meaningful behaviour-change post-intervention; about 35% show modest change; about 40% show minimal change. This distribution reflects the realistic efficacy patterns reported in the literature.
The bias profiles and anxiety scores in the corpus reflect the published research on demographic and life-stage variation in behavioural-finance patterns — without reducing those variations to caricature. The Methodology PDF documents the calibration approach so users can understand where the patterns come from.
Households mid-divorce or post-loss carry structured behavioural-trajectory data showing how anxiety, spending, and bias profiles change through the event. The post-loss avoidance pattern (where surviving-spouse households temporarily disengage from financial planning) is structurally documented for the queries that surface intervention windows.
Yes. About 35% of the corpus's lifestyle-inflation patterns are linked to specific events — promotion, marriage, home purchase, inheritance, business success. The structured event-driven inflation patterns let your tools surface the planning-conversation windows where catching the inflation early matters most.
B27 focuses on transitions (divorce, widowhood, sudden wealth) with structured event clustering. B30 focuses on the behavioural patterns themselves — anxiety, overconfidence, avoidance, lifestyle inflation. The two are complementary: B27 surfaces the windows; B30 explains the behaviour. Many platforms purchase both for integrated transition-and-behaviour coverage.
220 households mid-transition: divorce in progress, post-bankruptcy recovery, medical-debt crisis, sandwich-generation caregivers, recent windowhood, sudden wealth, distressed mortgages, and blended-family formation. High behavioral-event density and asset reshuffling.
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).
180 households with detailed student loan data: loan types, servicers, IDR plan enrollment, PSLF qualifying-payment counts, refinancing history, and forgiveness-tax-bomb projections. Includes Parent PLUS borrowers and double-consolidation paths.
Purchases are for internal use only. Redistribution or resale of data is prohibited under the WealthSchema Data License.
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