Jennifer Azubuko

Validated opportunity for a trust-first AI finance assistant in Nigeria, shaped a localized go-to-market strategy, and outlined MVP pathways for fintech integration.

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Overview

This concept project explored how artificial intelligence could help Nigerians manage money smarter — not by automating finances, but by earning their trust first.

In a market where people often fear losing funds to “AI errors” or “digital scams,” I set out to design and validate a financial assistant that feels human, helpful, and culturally grounded.

This concept project explored how artificial intelligence could help Nigerians manage money smarter — not by automating finances, but by earning their trust first.

In a market where people often fear losing funds to “AI errors” or “digital scams,” I set out to design and validate a financial assistant that feels human, helpful, and culturally grounded.

Objective

To use market and user insights to shape a credible product concept and a smart, low-risk go-to-market plan.

Problem

Nigeria’s fintech space is crowded with savings and lending apps, but very few help people understand their money.

Users want financial clarity, not just automation.

So the question became:

“What would an AI financial assistant look like if it was truly built for Nigerians — grounded in trust, local context, and daily realities?”

Nigeria’s fintech space is crowded with savings and lending apps, but very few help people understand their money.

Users want financial clarity, not just automation.

So the question became:

“What would an AI financial assistant look like if it was truly built for Nigerians — grounded in trust, local context, and daily realities?”

Research & Insights

1. Market Analysis

I analyzed six leading fintech apps (PiggyVest, Cowrywise, Kuda, FairMoney, etc.) and discovered:

Market saturation in savings and micro-lending.

Gaps in personalized guidance and financial education.

Poor differentiation — most brands promise the same thing: “Save more.”

1. Market Analysis

I analyzed six leading fintech apps (PiggyVest, Cowrywise, Kuda, FairMoney, etc.) and discovered:

Market saturation in savings and micro-lending.

Gaps in personalized guidance and financial education.

Poor differentiation — most brands promise the same thing: “Save more.”

1. Market Analysis

I analyzed six leading fintech apps (PiggyVest, Cowrywise, Kuda, FairMoney, etc.) and discovered:

Market saturation in savings and micro-lending.

Gaps in personalized guidance and financial education.

Poor differentiation — most brands promise the same thing: “Save more.”

2. User Discovery

I conducted 15 quick interviews and polls to understand what users really want from money apps.

Findings:

73% fear “AI mistakes” or losing control of funds.

80% prefer chat-based experiences to complex dashboards.

Users want budgeting help and gentle nudges, not just savings automation.

2. User Discovery

I conducted 15 quick interviews and polls to understand what users really want from money apps.

Findings:

73% fear “AI mistakes” or losing control of funds.

80% prefer chat-based experiences to complex dashboards.

Users want budgeting help and gentle nudges, not just savings automation.

2. User Discovery

I conducted 15 quick interviews and polls to understand what users really want from money apps.

Findings:

73% fear “AI mistakes” or losing control of funds.

80% prefer chat-based experiences to complex dashboards.

Users want budgeting help and gentle nudges, not just savings automation.

Concept Development

Using insights from market and user research, I shaped three concept pillars:

1. Smart Budget Coach

Gives daily spending insights in local currency and language.

2. Trust Score System

Shows users how AI makes recommendations, improving transparency.

3. Goal Companion Chatbot

Conversational interface that helps users plan goals, celebrate milestones, and build consistency.

Using insights from market and user research, I shaped three concept pillars:

1. Smart Budget Coach

Gives daily spending insights in local currency and language.

2. Trust Score System

Shows users how AI makes recommendations, improving transparency.

3. Goal Companion Chatbot

Conversational interface that helps users plan goals, celebrate milestones, and build consistency.

Using insights from market and user research, I shaped three concept pillars:

1. Smart Budget Coach

Gives daily spending insights in local currency and language.

2. Trust Score System

Shows users how AI makes recommendations, improving transparency.

3. Goal Companion Chatbot

Conversational interface that helps users plan goals, celebrate milestones, and build consistency.

Strategy & GTM Plan

Positioning:

“Your AI Money Buddy — simple, safe, and Nigerian.”

Positioning:

“Your AI Money Buddy — simple, safe, and Nigerian.”

Positioning:

“Your AI Money Buddy — simple, safe, and Nigerian.”

Distribution:

Start with a WhatsApp prototype for early testing and trust-building.

Leverage micro-influencers and financial coaches for organic traction.

Distribution:

Start with a WhatsApp prototype for early testing and trust-building.

Leverage micro-influencers and financial coaches for organic traction.

Distribution:

Start with a WhatsApp prototype for early testing and trust-building.

Leverage micro-influencers and financial coaches for organic traction.

Monetization:

Freemium model — free budgeting tips, premium personalized coaching.

Monetization:

Freemium model — free budgeting tips, premium personalized coaching.

Monetization:

Freemium model — free budgeting tips, premium personalized coaching.

Outcome

✅ Validated opportunity for an AI finance assistant rooted in cultural trust and transparency.

✅ Designed a user-centered concept that bridges education, behavior, and empowerment.

✅ Mapped out MVP and GTM paths adaptable to existing fintech ecosystems.

Key Takeaway:

AI adoption in emerging markets isn’t a technology problem — it’s a trust problem.

Building human-centered AI means designing for emotion, culture, and control.

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