Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

AshleyNyaboke/sme-payment-adoption-predictor

Domain:

socioeconomicdigital infrastructure

Record type:

modelsoftware
Creator:
Ash
Host:
ML tool that predicts SME digital payment adoption for Absa Bank Kenya & Airtel Money Kenya # 🇰🇪 SME Digital Payment Adoption Predictor > Built specifically for the **Absa Bank Kenya & Airtel Money Kenya** > strategic partnership on digital payments for SMEs. ## 🎯 Project Overview Millions of Kenyan SMEs still rely on cash for daily transactions despite the availability of digital payment infrastructure. This project uses machine learning to predict which SMEs are most likely to adopt digital paybill payments — helping financial institutions like Absa and Airtel target the right businesses with the right interventions at the right time. **Live Dashboard:** ("sme-payment-adoption-predic…") ## 💡 Business Context | Company | Relevance | |---|---| | **Absa Bank Kenya** | Investing KES 3 billion annually in digital banking. Paybill 303030 is a key growth channel for SME payments | | **Airtel Money Kenya** | Recently separated into Airtel Money Kenya Limited. Partners with Absa to allow payments via *334# and My Airtel App with 100% cashback incentives | Instead of marketing digital payments to every SME blindly — which is expensive — this model helps both companies prioritize the highest probability adopters, saving marketing budget and improving conversion rates. --- 📊 Model Performance | Model | Accuracy | |---|---| | Logistic Regression | 86.00% | | **Random Forest** | **89.00% ✅ Best Model** | ### Detailed Scorecard (Random Forest) | Metric | Non-Adopters | Adopters | |---|---|---| | Precision | 86% | 90% | | Recall | 74% | 95% | | F1-Score | 79% | 93% | ### Key Finding Mobile money transaction frequency is the single strongest predictor of SME digital payment adoption — stronger than revenue, business age, or location. When the model predicts an SME will adopt, it is right 90% of the time, and it catches 95% of all actual adopters. This is a directly actionable insight for Absa and Airtel's marketing teams. --- ## Feature Importance The top drivers of SME digital payment adoption discover …

Visit

github.com

Similar

Qamar-usman-ai/SME-Financial-Health-PredictorAshleyNyaboke/wallet-price-warAshleyNyaboke/Kenya-Carbon-Fintech-AnalysisAshleyNyaboke/mobile-money-fraud-detectionSaaS ERP Adoption Intent: Explaining the South African SME PerspectiveHuman-Centred AI Adoption for Sustainable SME Growth in Zimbabwe

Qamar-usman-ai/SME-Financial-Health-Predictor

A high-performing classification pipeline predicting the Financial Health Index of Southern African

AshleyNyaboke/wallet-price-war

Interactive analysis of Kenya's M-Pesa vs Airtel Money business wallet pricing war real 2026 tariff

AshleyNyaboke/Kenya-Carbon-Fintech-Analysis

Kenya carbon fintech data model & ETL pipeline tracking results-based mobile micro-payouts vs. ecolo

AshleyNyaboke/mobile-money-fraud-detection

AI fraud detection system for Kenya's mobile money ecosystem — 98% accuracy, built with Isolation F

SaaS ERP Adoption Intent: Explaining the South African SME Perspective

Part 1: Full Papers International audience This interpretive research study explores

Human-Centred AI Adoption for Sustainable SME Growth in Zimbabwe

Artificial intelligence (AI) is increasingly transforming how small and medium-sized enterprises (SM