Building a Credit Scoring Model: A Practical Guide for Emerging Markets

Question

Introduction

In many emerging markets, traditional banking data is scarce, but mobile money usage is high. Services like M-Pesa,G-Money, GCash,Monzo, Revault to mension but a few have become financial lifelines, yet users still struggle to access credit due to the absence of formal credit histories.

As a DevOps and ML practitioner passionate about practical AI solutions, I built an open-source project that uses mobile money transaction data to generate explainable credit scores, even in low-data environments.

In this post, I’ll walk through how it works, the tools I used, and how you can replicate or build upon it.

Problem: Credit Scoring Without Traditional Data

In many African and emerging economies:

• Credit bureaus don’t cover most of the population
• Many people operate entirely through mobile money
• Lenders lack reliable tools for assessing borrower risk

That’s where machine learning and alternative data sources like transaction frequency, balance trends, airtime top-ups, and withdrawal patterns come into play.

Tools & Stack

• Python & Jupyter Notebooks for model prototyping

• Pandas & Scikit-learn for feature engineering and modeling

• KMeans Clustering to segment users

• Decision Trees for transparency

• SHAP & LIME for explainability

Everything is open-sourced and designed to be easy to run on a laptop, no cloud costs, no heavy dependencies.

How the Model Works

Data Simulation

• Synthetic transaction logs modeled after real-world M-Pesa usage

• Columns include transaction types, amounts, frequencies, and balance history

Feature Engineering

Metrics:

• Number of transactions per month

• Average balance duration

• Variance in top-up behavior

• Frequency of peer-to-peer transfers

KMeans Clustering

• Groups users into behavioral segments

• Helps identify low-risk vs high-risk borrower patterns

Decision Tree

• Simple classification model based on key features

• Easy to interpret for non-technical stakeholders

Explainability

• Use SHAP to visualize what influenced each score

• Use LIME to show local prediction explanations for individual users

Why This Matters

• AI must solve real problems for real people.

• Enable fintechs and startups to assess borrower risk affordably

• Promote financial inclusion through technology

• Encourage transparent machine learning in sensitive domains like credit

Try it your self:

Follow the Github Link below to fork the repo, clone to reuse the code.

https://github.com/cliffordisaboke/mpesa-credit-score-demo

Don't forget to star the repo if the code is useful to you!

Comments

Love how this tackles real-world challenges with practical tools—huge kudos for open-sourcing it! Curious though, have you tested how well the model generalizes across different mobile money platforms like GCash or Monzo, or is it currently tuned mostly for M-Pesa-like behavior?

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Thank you so much, really appreciate the kind words and thoughtful question!
At the moment, the model is tuned primarily around M-Pesa-like transaction behavior, based on simulated logs that reflect common mobile money usage patterns in East Africa. That said, I designed the pipeline to be modular, and I’m actively exploring how it could generalize to other platforms like GCash, Monzo, and even Revault.
Each of these services has different transaction types and user behaviors, so adapting the feature engineering step is key. I’m currently experimenting with a plugin-style data ingestion layer to make the model more flexible across providers.
Would love to hear from others who’ve worked with mobile money data in different regions, collaboration opportunities are always welcome!