AI-Personalised Wealth Advisor for Digital Banking
Designing a hyper-personalised AI wealth advisory experience that turned generic robo-advice nobody acted on into recommendations customers actually followed, at a 68% adoption rate.
↑ Advice reframed around a person's actual goal, not a generic portfolio template.
The generic advice problem
Our digital bank had built a robo-advisor product, but engagement was catastrophically low. Users logged in, saw a generic portfolio recommendation like "consider a balanced fund," and immediately dropped off. The AI was generating useful output. The UX simply failed to connect it to any individual customer's actual life goals, risk appetite, or financial context.
Only 12% of users who received AI-generated advice took any action, against an industry average of 31%. The same five advice templates were served to all 200,000+ users regardless of income, life stage, or existing holdings. In research, 64% of customers said the advice felt like it was written for someone else.
Designing advice that feels personal, not algorithmic
The brief: design an AI advisory experience that adapts to each user's life goals, financial context, risk profile, and behaviour, delivering advice so contextually relevant that it reads as if a human adviser wrote it with the customer's full picture in front of them.
"Priya," 28, Emerging Investor
"The advice doesn't know anything about what I actually want to do."
"Marcus," 44, Wealth Accumulator
"The AI doesn't factor in my ISA allowance or my existing portfolio allocation."
Three compounding failures in generic advice
Same advice, every customer
Five advice templates served over 200,000 users with wildly different incomes, ages, and goals. A 29-year-old saving for a deposit and a 55-year-old approaching retirement got the same recommendation.
Zero visible reasoning
Recommendations arrived as flat statements with no explanation of why the AI suggested them, so customers had no way to judge whether the advice actually applied to their situation.
Too much friction to act
Even the 12% of users who trusted a recommendation faced a multi-step form to actually execute it, bleeding off intent before it turned into action.
How might we make every piece of AI advice feel like it was written for this specific person's goal, and make acting on it take one tap, not one form?
If we anchor every recommendation to the user's declared or inferred goal, show the reasoning behind each suggestion, and collapse the path from advice to action to a single tap with an easy undo, then advice adoption will rise sharply and customers will start trusting the AI as a genuine adviser rather than a generic engine.
Watching trust disappear, one step at a time
The engagement funnel before redesign told its own story: most users saw the advice, far fewer read it, and almost none acted on it. Each drop-off point needed a different fix.
↑ Based on 200,000+ user events, six months pre-redesign. Bar length is relative to the first stage.
Behavioural Interviews · n=24
Moderated sessions with current and lapsed wealth users on what "good advice" meant to them, and what made them distrust the AI.
Data Signal Mapping
Worked with data science to map 14 in-app behavioural signals that could personalise advice without asking users for more input.
Responsible AI Review
Partnered with compliance on guardrails ensuring every recommendation stayed within FCA guidelines and disclosed its AI origin.
What 24 interviews made obvious
Nobody wants portfolio advice. They want their goal to arrive sooner
Priya didn't care about equity exposure. She cared about her house deposit timeline. Framing advice around the goal, not the instrument, was the single biggest shift in how people received it.
Distrust came from invisibility, not disagreement
Customers rarely disagreed with the substance of the advice. They distrusted advice they couldn't see the reasoning behind, since an unexplained suggestion is indistinguishable from a guess.
Two very different customers needed two very different defaults
Priya and Marcus weren't edge cases of each other. A 28-year-old saving for a deposit and a 44-year-old managing existing holdings needed genuinely different starting assumptions, not one dial turned up or down.
The rules we designed by, and why
1 · Anchor every recommendation to a real goal
Derived from interviews showing goal-framing mattered more than instrument-level detail.
→ Advice cards state impact in terms of the user's own goal timeline, never in isolation.
2 · Show your reasoning, or lose trust
Derived from distrust being driven by invisibility, not disagreement with the substance.
→ Every card includes a collapsible "Why this advice?" panel naming the signals behind it.
3 · Reduce action to one tap, with an easy way back
Derived from users abandoning multi-step forms even after deciding to act.
→ A single "Do This" action, with a 30-second undo window instead of an upfront confirmation form.
4 · Treat life stages as different products, not one dial
Derived from Priya and Marcus needing fundamentally different starting assumptions.
→ Persona-informed defaults feeding the personalisation model, not a single generic profile.
From behavioural signals to a advice card that converts
01 Iteration 01 · Synthesis Turning 24 Interviews into Two Personas
Rather than one generic user profile, the interview data clustered into distinct groups with genuinely different needs. Priya and Marcus became working personas the whole team referenced when debating a design decision, keeping "which user does this help?" concrete instead of abstract.
02 Iteration 02 · Concept Rewriting Advice Copy Around Goals
Working with the 14 mapped behavioural signals, I rewrote advice copy from instrument-first ("consider increasing equity exposure") to goal-first ("this shortens your house deposit timeline by 8 months"). The same underlying recommendation, framed around what the customer actually cared about.
03 Iteration 03 · Validation A/B Testing the Reasoning Panel and One-Tap Flow
Cards with the "Why this advice?" reasoning panel produced 2.4 times higher action rates than identical cards without it in A/B testing. Separately, replacing the multi-step confirmation form with a single "Do This" action plus a 30-second undo window lifted completion from 12% to 68%.
The choices that shaped the outcome
Decision 01 · Goal-first copy vs. standard financial terminology
Tension: Compliance needed precise regulatory language present on every recommendation.
Choice & trade-off: We kept every required disclosure intact, but wrapped it in goal-first framing rather than leading with it, satisfying compliance without burying the message customers actually needed to hear first.
Decision 02 · Collapsible reasoning panel vs. always-visible reasoning
Tension: Showing full reasoning by default adds visual density to every card, even for users who don't want it.
Choice & trade-off: We made it collapsible but prominent, so users choose their own depth. Its mere presence, not constant visibility, was what drove the trust lift.
Decision 03 · Delayed execution with undo vs. instant execution
Tension: Instant execution is simpler to build and feels more decisive than a delayed, reversible action.
Choice & trade-off: We chose the 30-second undo window. A small delay was a fair trade for protecting users from an irreversible financial mistake made in a single tap.
A dashboard built around one person's financial story
The wealth dashboard was redesigned around the customer's own financial narrative, not a product catalogue. The advice feed ranks by personalisation score, so the most relevant card to that user's current situation surfaces first.
Move £500 to a Cash ISA
Saves you £120 in tax this year. Reaches your goal 2 months sooner.
Advice based on your goals, spending patterns, and risk profile
↑ Goal Progress Hero above a Personalised Advice Feed card, with the "Why this advice?" reasoning built into every recommendation.
Goal Progress Hero
Visual timeline of current trajectory versus goal target, with a projected completion date.
Why This Advice Panel
Collapsible explainer showing the data signals driving each recommendation.
Scenario Planner
"What if I invested £200 more a month?" tool anchored directly to the goal timeline.
Design doesn't happen in isolation
Data science: mapping signals responsibly
Data science helped identify which of the 14 behavioural signals were reliable enough to personalise advice on, and which were too noisy to trust with real recommendations.
Compliance: keeping personalisation within FCA guidelines
Compliance reviewed every advice template to confirm goal-first framing didn't drift from what was actually regulated, defensible guidance.
Investment product team: validating the numbers behind the copy
The product team checked that statements like "reaches your goal 2 months sooner" were calculated consistently across every advice card type, not just the ones we tested most.
How we arrived at the solution
Jobs-To-Be-Done (JTBD)
Interviewed customers to understand the emotional drivers behind investing, moving past functional requirements.
Kano Model Prioritisation
Worked with stakeholders to classify features into Basic, Performance, and Excitement categories ahead of the MVP launch.
Illustration Placeholder
Prompt: A stylized illustration of a wealth-management design workshop, a Kano model diagram and clean UI wireframes on a whiteboard, in warm amber and gold tones. Rendered as a clean illustration on a fully transparent background (no backdrop, scene, or color fill), so it displays cleanly on both light and dark page themes.
Advice people actually act on
"For the first time an app actually understood that I'm saving for a house, not retirement. The advice made sense for my life. I moved money within 5 minutes of opening the app."
Priya S., Retail Banking Customer
Honest reflections from the process
What Worked
Building real personas instead of one composite user
Priya and Marcus kept design debates concrete. Instead of arguing about "the user," the team argued about whether a specific change helped Priya, Marcus, both, or neither.
A/B testing the reasoning panel instead of assuming transparency would help
The 2.4x action-rate lift gave the team a concrete number to defend the extra design and engineering work the reasoning panel required.
What I'd Do Differently
Recruit interview participants beyond our two dominant personas earlier
Priya and Marcus were strong, clear personas, but they don't represent every customer on the platform. I'd widen the interview pool earlier to catch life stages that didn't fit either pattern.
Track long-term financial outcomes, not just adoption rate
Advice adoption and NPS are strong proxies, but the real test is whether customers who acted on personalised advice actually ended up better off financially a year or two later. I'd push for that longer-horizon measurement from the start.