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Module 1: AI fundamentals for insurance professionals

Welcome: AI governance for insurance and how this course works
What AI is (and isn’t) in an insurer
How a claims fraud model learns
The AI lifecycle in an insurer
Generative and agentic AI in insurance
How AI fails
Measuring performance: why “97% accurate” can mislead
The Africa lens and ten questions to ask
Module 1 quiz

Module 2: AI across the African insurance value chain

Where AI sits in the insurance value chain
Mobile, embedded and microinsurance
Parametric and agricultural insurance
Life, health and takaful
Insurtechs, partners and who is accountable
Mapping opportunity and harm
Module 2 quiz

Module 3: Ethics, fairness and treating customers fairly

Responsible AI principles for insurers
What fairness means in insurance
Measuring fairness
Treating Customers Fairly when AI is involved
Transparency, explanation and human review
Vulnerable customers and inclusion
Module 3 quiz

Module 4: Insurance AI regulation in Africa

The regulatory landscape for insurance AI in Africa
Data protection law and insurance AI
What insurance supervisors expect
International laws and standards that reach African insurers
Mapping obligations for an AI use case
Assignment 1: A regulatory obligations map
Module 4 quiz

Module 5: Building an AI governance framework for insurers

What an AI governance framework is for
Roles, committees and three lines of defence
The AI inventory
Risk tiering in depth
Proportionate controls for each tier
Governance for small insurers and insurtechs
Module 5 quiz

Module 6: Data governance, privacy and alternative data

Data quality and representativeness
Alternative data: opportunity and limits
Lawful basis, consent and transparency in mobile insurance
Data protection impact assessments for AI
Cross-border data, cloud and vendors
Privacy and security techniques for AI
Module 6 quiz

Module 7: Fair underwriting and pricing

How AI is changing underwriting and pricing
Proxy discrimination in pricing
Price optimisation and willingness to pay
Health, life and sensitive data in underwriting
Validating a pricing model
A method for fair pricing reviews
Assignment 2: A fair pricing review
Module 7 quiz

Module 8: Claims, fraud and redress

AI in the claims journey
Automatic claims decisions and human review
Detecting fraud fairly
Documents, images and deepfakes
Complaints and redress for AI decisions
Monitoring claims outcomes
Module 8 quiz

Module 9: Distribution, generative AI and conduct risk

AI in distribution and sales
Designing safe customer chatbots
Local languages and voice
Generative AI for staff, agents and brokers
Security risks of customer-facing AI
Monitoring conduct risk in AI distribution
Module 9 quiz

Module 10: AI policy, procurement and shadow AI

Putting up an organisational AI policy
Drafting, approving and rolling out the policy
Buying AI: the procurement lifecycle
AI clauses in vendor contracts
Shadow AI: what it is and how to find it
Responding to shadow AI
Writing acceptable-use rules for generative AI
Assignment 3: AI policy, procurement and shadow AI
Module 10 quiz

Module 11: Monitoring, evaluation and compliance assurance

Monitoring AI in use
Drift and AI incidents
Evaluating whether AI delivered
Internal compliance assurance
Three lines in practice and auditing AI
Reporting, inspection readiness and improvement
Assignment 4: A monitoring and compliance assurance plan
Module 11 quiz

Module 12: Final exam and capstone

Putting it all together: a 12-month roadmap
Final exam
Capstone: an AI governance programme for Baobab Insurance Group
Where to go from here

Length: About 45 hours, self-paced
Lessons: 87
Language: English

Insurers across Africa are using AI to price policies, detect fraud, settle claims and talk to customers on WhatsApp. Done well, AI can bring affordable cover to millions of people who have never been insured. Done badly, it can deny fair claims, price people out and break the law, at scale. This course trains insurance professionals to govern AI so it does the first and not the second.

Who it’s for

Compliance, risk and legal officers, actuaries, underwriters, claims and product managers, insurtech founders, brokers and bancassurance partners, internal auditors and insurance supervisors. No technical background or coding is needed: everything is built up from first principles, with insurance examples throughout.

What you’ll be able to do

Explain how machine learning, generative AI and AI agents work in insurance, and how they fail.
Map where AI is used across the insurance value chain, including mobile, embedded and microinsurance.
Apply fairness and Treating Customers Fairly principles to underwriting, pricing and claims.
Identify the data protection, insurance conduct and AI rules that apply in African markets, and international standards such as IAIS guidance and ISO/IEC 42001.
Design an AI governance framework for an insurer: roles, risk tiering, inventory and controls.
Write an organisational AI policy, run risk-based AI procurement and bring shadow AI under control.
Monitor and evaluate AI systems in use and provide internal compliance assurance.

How it works

Twelve modules, about 45 hours in total, including assignments and the capstone, at your own pace. Each module mixes short readings with interactive practice set in fictional African insurers: scenarios, audits, sorting and policy-building. Practice is never marked.

Assessment: a quiz after each module, four applied assignments marked against a published rubric, a timed final exam, and a capstone in which you design AI governance for a fictional African insurer, with peer review and assessor marking.

Certificate

Complete every step to earn the Certificate in AI Governance for Insurance, a verifiable digital certificate and Open Badge. Your capstone result (pass, merit or distinction) is shown on it.

Certificate and badge

Completed twelve modules covering AI fundamentals, AI in the insurance value chain, fairness and customer outcomes, insurance AI regulation in Africa, governance frameworks, data governance, fair underwriting and pricing, claims and fraud, distribution and generative AI, AI policy, procurement and shadow AI, and monitoring, evaluation and compliance assurance; passed every module quiz, four rubric-marked applied assignments and a timed final exam; and completed an assessor-marked capstone.

Instructor

Pacfaie Courses

AI Governance for Insurance in Africa