🧠 STATUS: Learning-Focused | Exploratory Case Study | Not a Production Design
This repository contains an exploratory product management case study focused on reducing user uncertainty during regulated fintech onboarding, particularly in identity verification (KYC) stages.
The goal of this project is not to redesign or critique any specific company's product, but to demonstrate a structured, constraint-aware approach to product discovery and AI-assisted experience design in regulated environments.
Repository: https://github.com/VIKAS9793/Fintech-Onboarding-Clarity
Read the Complete UX Case Study →
Explore how thoughtful UX and UI design can reduce friction and anxiety during fintech onboarding—especially when verification fails—through clear, trust-first design principles.
View the live design progression from low-fidelity wireframes to high-fidelity UI, complete with flow diagrams and UX principles documentation.
- Start with docs/01-context.md — Understand the domain and scope
- Read constraints before ideas — docs/04-constraints-non-goals.md
- Refer to visuals while reading each section — visuals/
- For quick overview — Executive Summary
- Watch the pitch — YouTube Video
Digital onboarding in fintech and banking products operates under strict regulatory, compliance, and fraud-prevention constraints.
While core verification systems are often optimized for risk control, some users experience uncertainty during edge cases such as verification retries.
This case study explores how a lightweight AI-assisted guidance layer could reduce cognitive friction without altering decision authority or compliance logic.
- Domain: Fintech / Banking
- Journey stage: Digital onboarding (KYC)
- Regulatory constraints are treated as non-negotiable
- AI does not approve, reject, or override verification decisions
This is a learning-oriented exploration, not a production proposal.
fintech-onboarding-clarity/
│
├── README.md
├── LICENSE
├── CONTRIBUTING.md
│
├── docs/
│ ├── 01-context.md
│ ├── 02-problem-statement.md
│ ├── 03-user-needs-jtbd.md
│ ├── 04-constraints-non-goals.md
│ ├── 05-assumptions-unknowns.md
│ ├── 06-product-approach.md
│ ├── 07-ai-decision-boundaries.md
│ ├── 08-metrics-success-criteria.md
│ ├── 09-risks-tradeoffs.md
│ └── 10-next-steps.md
│
├── deck/
│ └── slide-outline.md
│
├── visuals/
│ ├── context.png
│ ├── constraints.png
│ ├── ai-boundaries.png
│ ├── flow-overview.png
│ ├── metrics.png
│ └── trust-contract.png
│
├── summary/
│ └── one-page-executive-summary.md
│
└── methodology/
├── discovery-approach.md
└── use-of-ai-and-kiro.md
| Folder | Purpose |
|---|---|
/docs |
Core PM artifacts (problem, constraints, requirements, risks) |
/deck |
Presentation-ready case study slides |
/visuals |
Conceptual illustrations supporting the narrative |
/summary |
One-page executive summary |
/methodology |
Discovery approach and use of AI tools |
This project follows a constraint-first product discovery approach:
- Problem framing based on public user signals
- Explicit documentation of regulatory and operational constraints
- Clear separation of assumptions vs unknowns
- Minimal solution design with defined AI decision boundaries
- Metrics and validation planning
An AI-assisted IDE (Kiro) was used to structure documentation, surface assumptions, and stress-test reasoning. Final product judgments and decisions remain human-led.
- Executive Summary
- Problem Statement
- AI Decision Boundaries
- Metrics & Success Criteria
- Risks & Tradeoffs
Watch the full case study presentation:
- Product managers
- Fintech / banking product teams
- Interviewers evaluating PM judgment and thinking process
Vikas Sahani
- GitHub: @VIKAS9793
- LinkedIn: Vikas Sahani
- Email: VIKASSAHANI17@GMAIL.COM
Role: Product Manager
Scope: Product discovery, UX direction, and low-to-mid fidelity visual mockups
Tools: Figma (for rapid visualization and stakeholder alignment)
These designs are intentionally lightweight and exploratory. They are meant to communicate product intent, flows, and edge cases—not final visual design.
This project is licensed under the MIT License - see the LICENSE file for details.
What surprised me: Constraint discovery was more valuable than solution design. In regulated environments, knowing what you can't do is often more valuable than knowing what you could do.
What I'd validate next: Actual drop-off rates, failure category distribution, support ticket analysis, and user sentiment data — to determine if this solution is worth building at all.
Key takeaway: The best PM work isn't about having answers — it's about asking the right questions in the right order.
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
This project is an independent, exploratory case study. It does not represent internal data, roadmaps, or decisions of any company.
Built with structured thinking and AI-assisted documentation using Kiro IDE.