Introduction

We’ve all heard the stories of how banking used to be. During our recent research talk, Professor Babajide Samuel Afolabi from Obafemi Awolowo University reminded us of a time when sending money to a student meant handing cash to a bus driver and waiting 21 days for it to arrive or paying exorbitant fees for postal orders. While fintech has revolutionised speed and access, a significant gap remains: trust and true inclusion.

At Moniepoint, we pride ourselves on building reliable infrastructure for businesses. But as Professor Afolabi highlighted in his talk, "User Modelling for Enhanced Fintech Services," technology alone isn't enough. To truly serve the 25% of Nigerian adults who remain unbanked, we must move beyond standard demographics and build digital "mirrors" that reflect real human behaviour. This post summarises the key takeaways from his session, exploring how engineers can use data to bridge the gap between code and customer reality.

Key Concepts Covered

• User Modelling: Creating digital representations of users based on data and behaviour.

• The Trust Gap: The difference between urban risk-taking and rural reliability.

• Persona Development: Moving beyond "users" to specific profiles like "Amina" or "Chinedu".

• Behavioural Forensics: Using metadata (like MAC addresses) to detect fraud.

What is User Modelling?

Professor Afolabi defines user modelling as "building a mirror that helps us to see our users more clearly". It is not just about logging transactions; it is about creating a dynamic profile that helps in personalisation, prediction, and segmentation.

For engineers, this means shifting focus from what a user does (clicks, transfers) to who the user is.

• Urban Users: Prioritise speed and instant delivery. They are risk-takers but lack loyalty (trust is not in their vocabulary).

• Rural Users: Prioritise safety and reliability. They are sceptical, but once you earn their trust, they remain loyal indefinitely.

Why It Matters: The "Egg" Analogy

Professor Afolabi shared a powerful story about financial literacy. A woman was given capital to start a small business selling bread and eggs. On her first day, she became hungry, boiled six eggs, and ate a loaf of bread, consuming her capital before making a sale.

The Engineering Takeaway: We cannot simply "throw technology" at this problem. If a user doesn't understand that capital isn't revenue, a complex dashboard won't help them. We must design interfaces that guide behaviour, perhaps using voice prompts for the illiterate or simplified "safe-to-spend" balances.

3 Steps to Implementing User Modelling

Based on the talk, here is a framework for implementing better user models in our stack.

Step 1: Data Audit and Segmentation

We must look beyond standard transaction logs. Useful data sources include:

• Transaction frequency: Does the user withdraw immediately after receiving funds?

• Device usage: Are they on a smartphone or a feature phone?

• External data: Regulatory data and customer support interactions.

Action: Audit your current data pipelines. Are you capturing the "silence" from users who aren't providing feedback?

Step 2: Develop Detailed Personas

Don't design for "everyone." Design for specific archetypes. Professor Afolabi outlined three key personas relevant to our market:

1. Amina (The Rural Trader): Uses POS for daily sales, may have low literacy, needs voice prompts.

2. Chinedu (The Urban Student): Tech-savvy, seeks microloans, price-sensitive.

3. Ngozi (The MSME Owner): Needs payroll management and staff oversight tools.

Step 3: Predictive Modelling for Fraud

Behavioural modelling is our best defence against fraud.

• The Case Study: During a university exam, tracking shifted from IP addresses to MAC addresses. The team discovered that 100 students had submitted exams from the same device within 7 hours, exposing an AI-cheating ring.

• Application: In fintech, we can flag accounts when behavioural patterns (such as device usage or location) suddenly shift, helping catch fraud before it occurs.

A Mirror on Moniepoint: Strengths and Weaknesses

One of the most valuable parts of the session was Professor Afolabi’s candid assessment of Moniepoint’s current market standing.

What We Are Doing Right

• MSME Focus: We are correctly identified as the champion of the informal sector, including bread sellers and grocery owners.

• Reliability: Our POS terminals are often called "personal ATMs" because they continue to work when traditional bank networks fail.

• Simplicity: Users appreciate that our tools "just work" without unnecessary flashy features.

Areas for Improvement

• Customer Support: There is a perception of delayed responses. For a rural user, a delayed resolution immediately erodes trust.

• Dispute Resolution: Handling failed transactions and "hanging" funds remains a critical pain point.

• User Feedback Loops: We need to listen to what users are doing (e.g., uninstalling the app) as much as what they say on social media.

Troubleshooting & Discussion

During the session, several key questions arose regarding how we apply these theories.

Question: How do we reach the unbanked who don't trust banks?

Answer: Leverage the device they already trust: their phone. As of 2025, mobile penetration in Nigeria is at 87%, higher than the banked population. Solutions like M-Pesa succeeded by starting with basic USSD text services rather than complex apps.

Question: How can we improve UX for low-literacy users? 

Answer: Move beyond text. Research found that "Amina" (the rural trader persona) could easily navigate tricky interfaces when voice prompts were added.

Question: Is technology always the solution? 

Answer: No. Sometimes a user stops using an app simply because they don't understand the loan terms, not because the app is buggy. This is a communication gap, not a code bug.

Conclusion

Professor Afolabi left us with a striking calculation: If we capture 40 million users and earn just ₦100 per user per day, the revenue impact would be massive but only if we retain their trust.

User modelling is not just an academic exercise; it is the path to financial inclusion. By understanding the "Amina" in Wasimi and the "Chinedu" in Lagos, we can build products that are not just functional but essential. As we continue to refine our engineering roadmap, let’s remember: "Listen to the customer... Listen to what they are saying and what they are not saying".

You can watch the session here