AI-Personalised Wealth Advisor for Digital Banking
Designing a hyper-personalised AI wealth advisory experience that made retail banking customers act on AI financial advice achieving a 68% advice adoption rate, 3× portfolio growth, and £12M in new AUM.
The Situation
The Generic Advice Problem
When Every Customer Gets the Same Advice
Our digital bank had built a robo-advisor product but engagement was catastrophically low. Users logged in, saw generic portfolio recommendations ("Consider a balanced fund"), and immediately dropped off. The AI was generating useful outputs, but the UX failed to connect them to the individual customer's actual life goals, risk appetite, or financial context.
-
12% Advice Engagement Only 12% of users who received AI-generated advice took any action the industry average was 31%.
-
Zero Contextual Personalisation The same 5 advice templates were served to all 200K+ users regardless of income, life stage, or existing holdings.
-
Trust Deficit User research showed 64% of customers didn't trust the AI recommendations because they felt "like they were written for someone else."
Advice Engagement Before Redesign
Based on 200K+ user events 6 months pre-redesign
The Task
Design Advice That Feels Personal, Not Algorithmic
Emerging Investor — "Priya"
28 years old, Marketing Manager, £42K income
Priya wanted to start investing but found traditional robo-advisors intimidating and impersonal. She was saving for a house deposit in 3 years but the app kept recommending 30-year retirement portfolios. She needed advice framed around her actual goal — not generic wealth-building templates.
Wealth Accumulator — "Marcus"
44 years old, Director, £95K income
Marcus had existing investments but felt the app didn't understand his risk tolerance or tax situation. He wanted to optimise his portfolio for the current market cycle but the AI kept giving overly cautious generic advice that felt like it was built for someone 20 years younger.
The Hyper-Personalisation Challenge
Design an AI advisory experience that adapts to each user's life goals, financial context, risk profile, and behavioural patterns delivering advice so contextually relevant that users feel it was written specifically for them by a human adviser who knows their full financial picture.
The Action
Research, Personalisation Architecture, Validation
Behavioural Interviews
Conducted 24 moderated interviews with current and lapsed wealth users, focusing on their financial goals, what "good advice" meant to them, and what made them distrust algorithmic recommendations.
Data Signal Mapping
Worked with the data science team to map 14 behavioural signals available in-app (spending patterns, goal creation events, portfolio view frequency) that could personalise advice without requiring additional user input.
Responsible AI Review
Partnered with compliance to define guardrails for AI-generated advice ensuring recommendations stayed within FCA guidelines, disclosed AI authorship, and included human review escalation paths for high-value recommendations.
Three Core Personalisation Design Decisions
Goal-Anchored Advice Cards
Every advice item was reframed around the user's declared or inferred goal. Instead of "Consider increasing equity exposure," the card showed: "At your current rate, you'll reach your house deposit goal in 3 years 4 months. Moving 10% to a cash ISA would shorten that by 8 months." Goal-context made advice immediately actionable and personally relevant.
Transparent AI Reasoning Panel
Each advice card included a collapsible "Why this advice?" section that explained the signals driving the recommendation (e.g., "Based on your spending history, risk score 6/10, and 3-year goal horizon"). Making the AI reasoning transparent increased trust in A/B testing, cards with the reasoning panel had 2.4× higher action rates than cards without.
One-Tap Action with Confirmation Safety Net
Every advice card had a single primary CTA: "Do This." Tapping it launched a frictionless confirmation flow (not a full form) that showed the exact change, its projected impact, and a 30-second undo window. Removing multi-step friction from advice-to-action increased completion rate from 12% to 68%.
The Design
Personalised Wealth Dashboard
From Generic Recommendations to Personal Financial Stories
The wealth dashboard was redesigned around the customer's personal financial narrative not a product catalogue. The hero section showed progress toward their primary goal. The advice feed was ranked by personalisation score: advice most relevant to the user's current life situation surfaced first.
Goal Progress Hero — Visual timeline showing current trajectory vs. goal target with projected completion date
Personalised Advice Feed — AI-ranked cards with plain-language impact statements, confidence scores, and one-tap actions
Why This Advice Panel — Collapsible explainer showing the 3 data signals driving each recommendation
Scenario Planner — "What if I invested £200 more per month?" interactive tool anchored to goal timeline
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
The Result
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."