How is this course delivered?

This is a tutor-led course delivered live online, taught in English (Uganda). Your tutor guides you through the curriculum live rather than leaving you to self-study pre-recorded videos.

Will I get a certificate?

Yes. You'll receive a ai for financial inclusion certificate after completing the course requirements.

What do I need to know before I start?

You should have: Completion of Course:Supervised Learning in Depth; Completion of course:Unsupervised Learning & Clustering.

How long does the course take, and what does it cost?

The course runs for 6 weeks and costs UGX 650,000. Paying for one programming course also unlocks 25% off your next one.

What do I need to have ready to join?

You'll need: Laptop or desktop computer; Reliable internet access; Python 3; Jupyter Notebook or Google Colab; Spreadsheet software; Anonymised or synthetic financial transaction data.

Can I get help if I get stuck?

Yes. Enrolled students can book one-on-one tutor sessions any time through Get Help, in addition to regular class time.

Programming UniversityAI for Financial Inclusion

Machine Learning

University

AI for Financial Inclusion

Duration

6 weeks

Investment

UGX 650,000

Certificate

Included

Teaching

Live online

Enroll in this tutor-led course Get Help

Program Introduction

AI for Financial Inclusion equips learners to apply artificial intelligence and data analytics to financial services used across Uganda, East Africa and Africa. It focuses on mobile money, digital lending, SACCO and microfinance data, with practical attention to alternative credit scoring, fraud detection, customer segmentation, loan-default analysis, privacy, fairness and responsible deployment.

Key Features & Benefits

• Uganda, East Africa and Africa-focused fintech examples • Hands-on work with anonymised or synthetic transaction data • Practical SACCO and microfinance applications • Responsible AI, privacy and fairness checks • Capstone fintech AI prototype

Real-World Applications

• Design alternative credit-scoring support for SACCOs • Flag suspicious mobile money or digital lending transactions • Segment microfinance customers for better service design • Estimate loan default risk with fairness checks • Analyse savings and spending patterns • Support responsible fintech product development

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

AI for Financial InclusionFintech UgandaEast Africa FintechAfrican FintechMobile Money AnalyticsSACCOsMicrofinanceAlternative Credit ScoringFraud DetectionResponsible AIDigital LendingFinancial Inclusion Africa

Teaching format

Live online

Teaching language

English (Uganda)

Intended learners

University

What you will learn

Explain how AI supports mobile money, digital lending and inclusive finance
Prepare anonymised financial transaction data for responsible analysis
Build and evaluate an alternative credit-scoring workflow
Detect unusual or potentially fraudulent transaction patterns
Segment SACCO and microfinance customers using relevant data features
Develop a loan-default risk model and interpret its limitations
Create a savings and spending pattern analyser
Assess models for fairness, privacy, explainability and consumer-protection risks
Build and present a practical fintech AI prototype

Modules

1
AI in Modern African Fintech
Explain how AI supports mobile money, digital lending and inclusive finance in Uganda, East Africa and Africa
Financial inclusion conceptsAI in fintechMobile money ecosystemsDigital lending workflows
2
Alternative Credit Scoring
Use responsibly selected transaction, mobile money and airtime features, where lawful and appropriate, instead of relying only on formal bank history
Alternative dataFeature engineeringCredit-scoring workflowData quality and consent
3
Fraud Detection in Financial Transactions
Identify unusual transaction patterns and build an explainable fraud-screening workflow
Fraud patternsAnomaly detectionClassification metricsFalse positives
4
Customer Segmentation for SACCOs and Microfinance
Group customers using meaningful financial behaviour features to support better products and services
Segmentation goalsClusteringCustomer profilesResponsible use of segments
5
Responsible Loan-Default Risk Prediction
Develop a default-risk model and evaluate performance, fairness and limitations before use
Risk featuresModel trainingModel evaluationBias and fairnessExplainability
6
Savings and Spending Pattern Analyser
Build a tool that summarises cash-flow, savings and spending behaviour from transaction records
Transaction categorisationCash-flow trendsSavings indicatorsSpending patternsUser-friendly outputs
7
Regulatory and Ethical Considerations
Apply privacy, data protection, fairness, transparency and consumer-protection principles to fintech AI solutions
Uganda data protection contextConsent and data minimisationFair lendingModel transparencyHuman oversight
8
Capstone — Fintech AI Prototype
Build and present an alternative credit-scoring prototype for SACCO members using anonymised transaction history rather than formal credit records
Problem definitionData preparationModel developmentFairness checksPrototype presentation