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Abdulghafor Abbas.

Fintech / UI experiment / Concept

Cashi.

A digital wallet for young Sudanese users with limited data, varied literacy, and no patience for scrolling.

Send money in one tap, even if you can't read the menu.

Cashi wallet landing page on a laptop
Client
Concept project
Year
2025
Role
Product design, landing page and app
Tools
Figma, Framer, Perplexity

Problem

Cashi is a digital wallet concept for Sudanese users, with a strong focus on young people. The constraints are unusual and strict:

  • Many users have limited digital experience.
  • Literacy levels vary, and some people rely entirely on icons and images.
  • Connectivity is unreliable and data plans are expensive.
  • The home screen has to fit above the fold, with no scrolling.

A typical wallet home, with a dense grid of ten or more service tiles and text-heavy menus, would fail every one of these.

How I fixed it

I designed for recognition over reading. Every decision was tested against one question: could someone use this without reading a single word?

  • The two jobs people open a wallet for, Send money and Request money, became the largest targets on the screen.
  • Quick Send shows the people you pay most as photos, so choosing a recipient is recognizing a face, not reading a name.
  • Ten-plus service tiles collapsed into a few clear categories.
  • Clear icons, minimal text, and consistent spacing lower the effort of every screen.

Solution

A single-screen home that holds everything that matters, with no scrolling, which also cuts data use. The landing page explains the wallet with the same visual language: big actions, faces, and very little copy.

Cashi wallet app home screen on a phone

Results

This is a concept, so the results are design outcomes rather than measured metrics:

  • Faster person-to-person payments: fewer steps and less friction through centered actions and Quick Send avatars.
  • Lower cognitive load: services grouped into categories instead of 10+ tiles.
  • Usable without reading: icon-driven design and face recognition work for low-literacy users while staying light on data.

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