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!