Skip to content
Quest Training

AI Governance & Responsible AI for Insurance Training Course

The AI Governance & Responsible AI for Insurance Training Course provides a strategic and practical framework for governing the use of artificial intelligence across insurance organizations. The course focuses on…

INAI · InsurTech & Artificial IntelligenceAll LevelsClassroomEnglish , Arabic
Duration
5 Days
Download Brochure
Sectors:Insurance & Risk ManagementCategories:InsurTech & Artificial Intelligence

Course Overview

The AI Governance & Responsible AI for Insurance Training Course provides a strategic and practical framework for governing the use of artificial intelligence across insurance organizations. The course focuses on establishing effective governance structures, policies, controls, and accountability mechanisms that enable insurers to benefit from artificial intelligence while managing ethical, regulatory, operational, data, cybersecurity, and reputational risks. Artificial intelligence is increasingly being applied across underwriting, pricing, claims, fraud detection, customer service, distribution, risk assessment, and operational decision-making. These applications can create significant business value, but they also introduce risks related to bias, transparency, explainability, data quality, privacy, model performance, automated decisions, and accountability. The course addresses these challenges from an insurance-specific governance perspective. Participants will examine the principles of responsible artificial intelligence and learn how to establish governance throughout the artificial intelligence lifecycle, from identifying and approving use cases to model development, validation, deployment, monitoring, change management, incident response, and retirement. Particular attention is given to high-impact insurance decisions where artificial intelligence may influence customer outcomes, pricing, eligibility, claims, or risk classification. Through practical case studies, governance scenarios, risk assessments, policy-development exercises, and model oversight workshops, participants will develop the ability to establish responsible artificial intelligence frameworks, define roles and decision rights, assess artificial intelligence risks, strengthen controls, and create effective management and Board-level oversight mechanisms.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Analyze the strategic opportunities and risks associated with artificial intelligence in insurance.
  • Explain the core principles of responsible artificial intelligence and ethical AI governance.
  • Develop an enterprise-wide AI governance framework for an insurance organization.
  • Establish policies, roles, responsibilities, and accountability for AI use.
  • Identify and assess AI risks across the insurance lifecycle.
  • Evaluate risks related to bias, fairness, transparency, explainability, and discrimination.
  • Strengthen data governance, privacy, security, and model risk controls.
  • Establish appropriate governance for AI-enabled underwriting, pricing, claims, and fraud detection.
  • Develop AI approval, validation, monitoring, and escalation processes.
  • Establish controls for third-party AI solutions and technology providers.
  • Define appropriate levels of human oversight for automated insurance decisions.
  • Develop AI risk indicators, documentation standards, and management reporting.
  • Manage AI incidents, model changes, performance deterioration, and emerging risks.
  • Develop practical responsible AI policies and implementation roadmaps.

Who Should Attend

This course is designed for insurance executives, risk leaders, compliance professionals, technology and digital transformation managers, data and analytics professionals, internal auditors, legal professionals, and business leaders involved in the governance or implementation of artificial intelligence. It is particularly relevant to Chief Risk Officers, Chief Information Officers, Chief Digital Officers, Compliance Managers, Model Risk Managers, Data Governance Managers, Internal Audit professionals, Actuarial Managers, Underwriting Managers, Claims Managers, and senior decision makers responsible for ensuring that artificial intelligence is deployed safely, fairly, transparently, and responsibly.

Learning Outcomes

  • By the end of the course, participants will be able to:
  • Explain responsible artificial intelligence principles within the insurance context.
  • Identify major AI governance risks and control requirements.
  • Assess AI use cases according to business impact, risk, and customer implications.
  • Develop an AI governance framework with clear ownership and accountability.
  • Establish AI policies, standards, approval processes, and decision rights.
  • Assess risks related to bias, fairness, transparency, explainability, and discrimination.
  • Evaluate data quality, privacy, security, and model risk associated with AI systems.
  • Establish lifecycle controls for AI development, deployment, monitoring, and retirement.
  • Define human oversight requirements for high-impact automated decisions.
  • Develop AI monitoring, testing, documentation, and reporting mechanisms.
  • Assess risks associated with third-party AI models, platforms, and vendors.
  • Develop incident management and escalation processes for AI-related events.
  • Strengthen Board and executive oversight of AI risks and opportunities.
  • Establish continuous improvement mechanisms for responsible AI governance.
  • Develop a practical roadmap for implementing responsible AI governance within an insurance organization.

Course Outline

Course Outline

Day 1

Foundations of AI Governance in Insurance

  • Understanding artificial intelligence and its growing role in insurance.
  • Strategic applications of AI across the insurance value chain.
  • AI-enabled underwriting, pricing, claims, fraud detection, and customer services.
  • Opportunities and risks associated with AI adoption.
  • Principles of responsible and ethical artificial intelligence.
  • AI governance versus traditional technology governance.
  • Establishing organizational accountability for AI decisions.
  • Identifying high-impact and high-risk AI applications.
  • Practical Application: Develop an initial AI use-case inventory and classify applications according to business value and risk.
Day 2

Responsible AI, Ethics and Customer Protection

  • Principles of fairness, transparency, accountability, and explainability.
  • Identifying and managing algorithmic bias.
  • Fair treatment of customers in AI-supported insurance decisions.
  • Discrimination risks in underwriting, pricing, claims, and customer segmentation.
  • Explainability of AI-driven decisions.
  • Human oversight and human intervention requirements.
  • Customer communication and transparency regarding AI use.
  • Ethical considerations in automated insurance decisions.
  • Establishing responsible AI standards and controls.
  • Practical Application: Assess an AI-enabled insurance decision and identify potential bias, fairness, transparency, and customer-protection risks.
Day 3

AI Risk Management, Data Governance and Model Oversight

  • AI risk identification and assessment methodologies.
  • Data quality, data lineage, ownership, and governance.
  • Privacy and protection of sensitive customer information.
  • Model risk management and AI model validation.
  • Model performance, accuracy, robustness, and reliability.
  • Monitoring model drift and performance deterioration.
  • AI security and cybersecurity risks.
  • Documentation, testing, validation, and audit trails.
  • Managing AI risks across the full model lifecycle.
  • Practical Application: Conduct an AI risk assessment covering data, model, operational, privacy, security, and customer risks.
Day 4

AI Governance Frameworks, Controls and Third-Party Risk

  • Designing an enterprise AI governance framework.
  • Defining roles across the Board, executive management, business, risk, compliance, technology, and internal audit.
  • AI use-case approval and risk classification.
  • AI development, validation, deployment, and change-management controls.
  • Monitoring and escalation mechanisms.
  • Governance of third-party AI models, platforms, and technology providers.
  • Contractual, operational, data, and security considerations for AI vendors.
  • AI incident management and response.
  • Management and Board reporting for AI risk.
  • Practical Application: Design an AI governance and control framework for an insurance organization.
Day 5

Responsible AI Strategy, Implementation and Continuous Governance

  • Developing an enterprise responsible AI strategy.
  • Establishing AI policies, standards, procedures, and governance committees.
  • Integrating AI governance with enterprise risk management and compliance.
  • Developing AI risk indicators and governance dashboards.
  • Measuring AI performance, risk, compliance, and business value.
  • Managing AI incidents, regulatory changes, and emerging risks.
  • Building an AI-aware organizational culture and workforce capabilities.
  • Establishing continuous monitoring and improvement.
  • Developing an AI governance implementation roadmap.
  • Final Workshop: Design a comprehensive AI Governance & Responsible AI Framework for an insurance organization covering AI strategy, governance, risk classification, ethics, data governance, model oversight, human control, third-party risk, monitoring, reporting, incident management, and continuous improvement.

Upcoming Dates

No upcoming events are currently scheduled.

Request a Date

Related Courses

Ready to Elevate Your Team's Capabilities?

Speak with our advisors about upcoming programmes or a bespoke corporate training plan.

Download BrochureContact Our Team