Behavioural analytics for financial services

We measure how your clients
actually decide
.

Investor profiling, activation diagnostics, AI-assisted advisor support.

Weighted behavioural scenarios, a diagnostic engine, and an always-on nudge & advisor layer — turning board questions into evidence, explanation, and prescription on a named cohort.

Live with NTT DATA · Mirai 2026, 9–10 June, London

Evidence. Explanation. Prescription.

The investor profiling and activation diagnostic engine, in three deterministic layers. AI enriches the prose; it never invents a finding.

01

Profile

Nine archetypes built on competence × ambiguity tolerance, validated through weighted scenarios — not personality tests. Blindspot detection compares what people say about themselves against what their answers actually reveal.

02

Diagnose

A library of root-cause hypotheses tied to design levers you can actually move. “Why do women convert worse?” produces a different diagnostic than “why do 25–34 year-olds stall?” — with the upstream cause named, not guessed.

03

Prescribe

A specific intervention on a named segment, with a measurement plan. We commit to a 28-day check: did the cohort move past the stage, by how much, and was the lever the right one. If not, we say so.

And it keeps working — between every diagnostic

Behavioural nudges and AI-assisted advisor support, between sessions. The profile is the start, not the end — ongoing interventions move named cohorts in the background while one coherent investor view sits behind the chat.

Always-on layer

Behavioural nudges, always on

Once we know the archetype and the blindspot, we deliver tiny, contextual interventions in the channels your client already uses — checking a balance, opening the app, reading the newsletter. Each nudge is measured. The next diagnostic sees what landed and what didn’t.

One investor view

Chat, survey, portfolio — in one advisor view

Your AI copilot or human advisor sees every relevant signal at once: what the client revealed in the assessment, what their portfolio actually holds, what the conversation just surfaced. No more tab-switching between tools — one coherent investor view, built on the open Model Context Protocol (MCP) standard.

Behaviour-aware fund exploration

Three exploration surfaces we’ve shipped — designed around how humans actually navigate uncertainty, not how databases serialise. They’re examples, not the menu: we continuously design new ways for investors to engage with their money.

Star Map

Funds as a navigable universe

Eight hundred line items collapse into thirty stars, clustered by what the investor actually cares about. Decision fatigue drops; orientation appears. Choice architecture, applied to the fund universe.

Radar

What the client picks reveals what they value

Configurable axes for “distance” and “size” — ESG fit, ten-year return, drawdown, ticket size. As the client adjusts the lens, the interactions themselves become a behavioural signal, feeding the next session with what they actually look at.

DNA Helix

What the money is actually doing

Each fund becomes a double helix coloured by its top SDGs, drillable down to constituents. Investors stop seeing tickers and start seeing impact. The shift from abstract to concrete is itself a confidence intervention.

Three surfaces built on the same logic as the diagnostic: visuals that respect how humans actually think, paired with nudges that arrive in the moments choices are made. The result is exploration that closes the intention–action gap instead of widening it.

Built for the people asking the question

Wealth managers, fintech product teams, and investment platforms — if you have a board, a roadmap, or a quarterly conversion review, this is built for you.

Wealth managers & private banks
Understand your client base by investor archetype. Activate higher-conviction segments. Measure conversion uplift by profile and blindspot pattern.
Financial-services product teams
Embed client profiling into onboarding. Segment nurture by archetype. Ship targeted journeys with a measurement loop instead of A/B guessing.
Distributors & platforms
Run board-grade diagnostics on a customer cohort — activation gaps, drop-off stages, archetype skew — and export the intervention as a brief, not a slide.

Why this isn’t another quiz tool

Research rigour first. AI amplification second. In that order, on purpose.

Behavioural scenarios, not self-report

We measure how people behave under uncertainty — not what they say they’d do. The only way to detect the gap between intention and action.

Blindspot detection

We compare what a client believes about themselves with what their answers actually reveal — surfacing the gaps a Risk Tolerance Questionnaire never sees.

Deterministic scoring

The classifier is rules-based and reproducible. Same answers, same result, every time. AI may write the narrative, but never the numbers.

28-day measurement contract

Every prescription comes with a check-back. Did the named cohort move past the stage we said they would? If not, the hypothesis was wrong and we say so.

In production

“We’re embedding u impact’s Investor Dating Profile inside our Wealth Activation Platform for Mirai 2026 — 1,200 professionals, June 2026.”

Bring us a question your board keeps asking.

We’ll show you what the answer looks like — evidence, explanation, prescription — on a real cohort of yours.

Bring us the question