Lead Product Designer
Responsibilities:
Partner with Tech and Business
Product experience strategy
Defined end-to-end UX strategy
Ongoing nominations
Regulatory and compliance considerations for usage of data lakes and AI
Leading the design of an AI-driven experience that resolves corporate profile proliferation by surfacing existing matches before new profiles are created — reducing operational overhead for the bank and friction for clients
Open live website
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Identified a growing client need for intelligent, context-aware assistance within the banking platform — one that goes beyond static navigation to proactively support day-to-day tasks. Through stakeholder discovery and client engagement, I uncovered two critical pain points: clients struggling with routine, time-consuming tasks, and a systemic risk of dormant or suspended users retaining entitlements to sensitive products and services — a governance and compliance concern hiding in plain sight.
Defined the opportunity space across two parallel tracks: a conversational AI experience leveraging LLMs to assist clients seamlessly in their daily workflows, and an agentic AI model capable of proactively surfacing and alerting clients to access and entitlement risks within their organisation. Partnered with product owners and technology leads to scope the MVP, aligning capabilities with regulatory considerations, client readiness, and platform constraints.
Led ideation of core user flows that balance AI capability with client trust — designing for both the enthusiastic early adopter and the more cautious enterprise client. Explored generative AI integration points within mundane, high-frequency tasks to unlock meaningful productivity gains. Collaborated closely with engineers from A*STAR and internal data teams to understand how existing datasets could train and ground the AI model, ensuring outputs are relevant, accurate, and responsible
Translated ideated flows into tangible prototypes that bring the chatbot and agentic AI experience to life within the banking platform. Prototypes are designed to test both the conversational interface and the proactive prompt mechanics — specifically how the system surfaces dormant user alerts in a way that is actionable, non-intrusive, and compliant. Core flows are currently being refined in close partnership with technology and product stakeholders ahead of client testing
Adopted a deliberate testing strategy — prioritising clients who are actively anticipating AI within their banking experience to generate high-quality, forward-leaning feedback. This ensures the product is stress-tested against the highest expectations before broader rollout. Insights from this cohort will directly inform iteration cycles, with a continuous feedback loop designed to close gaps between client expectation and delivered experience — ensuring the platform does not fall short as AI adoption accelerates across the industry.
