Financial product design keeps repeating the same mistakes.

Every fintech team reinvents the same patterns. How should onboarding flow for a savings product? What does a trustworthy transaction confirmation look like? How do you present portfolio performance without triggering panic? These questions have answers — answers that exist in the best-performing products in the world — but there's no structured place to find them.

After a decade working across Luno (crypto), Allan Gray (investment), The Baymard Institute (ecommerce and financial UX auditing), and numerous client engagements, I found myself carrying an enormous amount of tacit knowledge about global financial product conventions. The database is my attempt to make that knowledge explicit.

"The patterns that make financial products trustworthy, usable, and commercially successful are surprisingly consistent across markets. The trick is knowing which conventions to follow and which to challenge."

What gets documented.

The database captures UX patterns, interaction norms, and design conventions across five categories of financial product: banking apps, crypto wallets, investment platforms, savings products, and money transfer. For each pattern, the entry records what the convention is, why it exists, where it works, where it breaks down, and which products execute it best.

Category 01
Banking apps — account overview, transaction history, transfers, cards, notifications. The oldest category and the most convention-bound. Deviation is often punished by user confusion.
Category 02
Crypto wallets — onboarding, key management, sending/receiving, exchange, portfolio view. The newest category and the most inconsistent. Big opportunity to establish better conventions.
Category 03
Investment platforms — portfolio overview, product discovery, ordering, performance presentation. Highest stakes for UX errors — bad design here has real financial consequences for users.
Category 04
Savings products — goal setting, progress visualisation, deposits, withdrawals, interest display. Emotionally charged domain — the UX needs to motivate behaviour, not just enable it.
Database structure — category taxonomy and entry schema
IA diagram · data model · Notion database architecture

Building the evidence base.

Each entry in the database is grounded in three sources: direct observation from live product audits, user research findings from my decade of testing and interviewing across financial contexts, and benchmark analysis comparing how different products in the same category approach the same challenge.

Comparative audit — three crypto wallet onboarding flows
Screen captures · annotation · pattern analysis

The benchmark analysis is particularly valuable. When ten different savings apps all make the same design decision, that's a convention worth understanding. When one app does something radically different and it works, that's a pattern worth documenting carefully — because it tells you something the consensus missed.

Pattern entry — transaction confirmation conventions across banking apps
Database entry · evidence · best practice examples

Knowledge that travels.

The database is designed to be used in three ways. As a research tool at the start of a project — instead of starting from scratch, you pull the relevant patterns and understand the conventions you're working within before you design anything. As a critique framework during design — checking decisions against established conventions and articulating why you're following or departing from them. And as a client communication tool — when recommending a change to a fintech product team, grounding the recommendation in a documented pattern makes it a design decision, not an opinion.

Client presentation — using database findings to support recommendations
Presentation deck · client workshop · recommendation framing
UX Research Heuristic Evaluation Benchmark Analysis Information Architecture Pattern Documentation Fintech Domain Crypto Investment Platforms Product Strategy Research Synthesis