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ADSS
Project
1. Role: Head of UX Research (Research + Experience Design Partner)
2. Industry: Regulated fintech / online trading
3. Products: Core Trading Platform + Social Trading Platform
4. Scope: 0→1 definition through MVP delivery, under strict compliance constraints
Context
ADSS is a regulated financial services company operating in a high-stakes, compliance-heavy environment where user error carries real financial and reputational risk.
I led UX research and partnered closely with product leadership, compliance, engineering, and design to shape two next-generation products built from the ground up: a primary trading platform and a social trading platform intended to support community-driven investing behavior.
The challenge
The hardest part wasn’t feature completeness. It was decision risk.
We needed to build from zero with:
• No existing UX baseline to iterate on.
• Strict compliance requirements that constrain experimentation and messaging.
• Complex concepts that require clarity without oversimplification.
• Low tolerance for ambiguity at critical moments (funding, placing orders, execution status, copying strategies).
The risk was building platforms that are technically compliant but fail to support confidence, trust, and informed decision-making, especially under time pressure and uncertainty.
What we learned (insight that reframed the work)
Users weren’t primarily struggling with trading mechanics. They were struggling with confidence and reassurance at decision points, particularly when uncertainty and perceived risk were highest.
This reframed the work from “simplify the interface” to support judgment under pressure, with compliant disclosure, clear system status, and strong error prevention/recovery.
Approach (how we worked in regulated 0→1)
I treated UX research as a decision engine for a regulated build:
• Align early with compliance so constraints are known upfront (not discovered late),
• Validate flows and information hierarchy before build commitments,
• Design around high-risk moments (where misunderstanding creates costly errors),
• Keep feedback loops tight so direction converges without expensive late rework.
Work ran as embedded research and design sprints tied to product planning, enabling rapid alignment across product, compliance, engineering, and design without sacrificing rigor.
Design outcomes (how it became real UI)
We translated confidence gaps into specific interaction and UI behaviors that could be built and scaled:
1. Decision-point architecture
• Mapped end-to-end journeys (onboarding → funding → market selection → order creation → execution → monitoring) and explicitly marked “high-risk moments.”
• Defined what must be visible before commitment (inputs, constraints, disclosures, confirmations) and what can be progressively disclosed.
2. Information hierarchy for trading confidence
• Structured trading tickets and review steps so users can quickly verify: instrument, size, leverage/risk implications, and “what happens next.”
• Standardized execution outcomes and system status cues (e.g., pending/filled/rejected/partial) so the interface is never ambiguous at critical moments.
3. Error prevention & recovery patterns
• Designed confirmation, validation, and rejection messaging that clearly explains why an action failed and what the user can do next, reducing guesswork and repeat attempts.
• Built clear exits, safe defaults, and editable steps to keep users in control.
4. Social trading trust mechanics (MVP)
• Defined the minimum viable trust model: what users need to decide whether to follow/copy, how performance and risk should be framed, and where UI elements could imply guarantees (requiring careful compliance handling).
• Designed the interaction logic for follow/copy/stop-copying and user controls as explicit, auditable actions.
Fidelity was used strategically:
Low-fidelity flows and state models accelerated alignment with compliance and engineering early, while high-fidelity prototypes were produced for the risk-heavy moments where hierarchy, microcopy, and interaction states drive confidence and error prevention, especially trading tickets, order review/confirmation, execution status states, and error messaging.
Deliverables
• Research synthesis pinpointing confidence gaps and risk moments
• Journey maps and decision-point inventories for critical flows
• Tested prototypes for trading and social trading journeys (happy paths & failure states)
• State models for execution outcomes and messaging rules
• MVP definitions aligned with regulatory feasibility
• Alignment packs used in product, engineering, and compliance reviews
Impact
• Optimized the usability testing framework, reducing costs by ~20% annually and generating ~$5M in savings through improved research operations and efficiency.
• Managed a team delivering 30+ UX research studies and design sprints across four fintech products, translating evidence into data-driven design improvements.
• Integrated customer journey mapping into delivery, enabling ~35% faster time-to-market for new features and improving overall satisfaction signals.
• Co-led executive strategy workshops that shaped a “10x” strategic vision and a concrete plan to address expansion within a regulated total addressable market.
Reflection
In regulated fintech, UX impact comes from precision: the right information at the right moment, unambiguous system status, and interaction patterns that prevent avoidable mistakes while staying compliant.
The most valuable work here wasn’t just “finding issues”, it was converting confidence risks into buildable, compliant UI behaviors and decision rules that teams could ship reliably.
NDA note
Visuals and details are simplified or reconstructed to preserve confidentiality while reflecting real system behavior, decision logic, and outcomes.
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