Fintech · Financial inclusion · 2025
Medad
Inclusive banking concept
A banking app concept that turns spending data into clear, personalised guidance for underserved users.
- Role
- AI & dashboard developer
- Status
- Concept
- Evidence
- Private project evidence
- Year
- 2025
Evidence
What a reviewer can check today — and what is not claimed.
- Status
- A product concept with dashboard designs. No public prototype is available.
- Evidence
Private project evidence
No public repository or demo is available, so nothing is linked.
- Not claimed
- No working app, users or financial outcomes are claimed.
- Stack
- Power BI
- Data visualisation
- AI analytics
Dashboards and analytics were designed as concepts; no implementation is published.
Context
A 2025 product concept exploring financial inclusion through AI-driven guidance.
The problem
Underserved communities face barriers to financial services and rarely get guidance that fits their situation.
Constraints
- Designed for people with limited financial literacy
- Guidance must stay understandable — insight, not raw numbers
- Concept stage: no live banking data
Solution
A banking app concept that uses AI-driven insights, dashboards and personalised recommendations to make financial guidance approachable.
My responsibility
AI & dashboard developer
- Contributed to the design of AI-driven insights
- Built dashboard concepts for decision visibility
- Supported the data visualisation and recommendation logic
- Connected product goals with the needs of inclusive users
Architecture & workflow
- 01 · Input
Spending data
- 02 · Processing
AI analytics
- 03 · Intelligence
Personalised insight
- 04 · Output
Inclusive guidance
How it works
- AI analytics to find patterns in spending behaviour
- Power BI dashboards for decision visibility
- Visualisations designed for low financial literacy
- Platform architecture planned to scale
Decisions
Inclusion first
Designed around the users who are usually an afterthought in banking products — the people who need guidance the most.
Insight over raw data
Analytics are turned into clear, personalised recommendations instead of numbers people have to decode.
Verified outcome
Dashboard and recommendation concepts for an inclusive banking experience. There is no public prototype, and no users, adoption or financial outcomes are claimed.
Limitations
- No public prototype or code.
- Recommendations have not been validated with real users or real financial data.
- Regulatory and data-protection requirements have not been worked through.
What would come next
- Prototype the guidance flow with synthetic transactions
- Test wording and trust with target users
- Review against banking regulations before any real data is used
The idea, sketched
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