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AI Governance Guidance

IBAC AI Working Group

The Al Guiding Principles are designed to support the broker community in reaching the following five objectives

1

Supporting Ethical Standards and Stakeholder Collaboration

2

Ensuring Consumer Trust and Fairness

3

Promoting Accountability in AI Oversight

4

Encouraging Responsible Al Innovation

5

Aligning with Regulatory Frameworks and Standards

KPMG's Trusted AI Framework was used as the foundation for developing the AI Guiding Principles

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AI solutions should be designed to reduce or eliminate bias against individual, communities, and groups.

AI Solutions should be designed to comply with applicable privacy and data protection laws and regulations.

Robust and resilient practices should be implemented to safeguard AI solutions against bad actors, misinformation, or adverse events.

AI solution should be developed and delivered in a way that answers the questions of how and why a conclusion was drawn from the solution.

Data used in AI solutions should be acquired in compliance with applicable laws and regulators and assessed for accuracy, completeness, appropriateness, and quality to drive trusted decisions.

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AI solutions should be designed to be energy efficient, reduce carbon emissions, and support a cleaner envoirment.

AI solution should be designed and implemented to safeguard against harm to people, businesses, and property.

AI solution should include responsible disclosure to provide stakeholders with a clear understanding of what is happening in each solution across the AI lifecycle.

Human oversight and responsibility should be embedded across the AI lifecycle to manage risk and comply with application laws and and regulations.

AI solutions should consistently operate in accoradance with their intended purpose and scope and at the desired level of precision.

Consolidated industry insights highlighted common key themes that further informed the AI Guiding Principles (1/2)

Consolidated industry insights highlighted common key themes that further informed the AI Guiding Principles (2/2)

Building on the industry-leading framework and insights, we have developed seven AI Guiding Principles, aligned with IBAC's values

1. Consumer Protection
2. Collaboration
3. Leadership
4. Sustainability
5. Advocacy & Representation
6. Community Engagement
7. Innovation & Technology
8. Professional Development

1. Safety, Security and Reliability

AI solutions are designed and implemented with robust and resilient practices that safeguard against threats, bad actors, misinformation, and adverse events, ensure safety for all stakeholders, and consistently operate at desired precision levels.

2. Privacy & Data Integrity

AI solutions and the data used are designed and acquired to comply with applicable laws and regulations and assessed for accuracy completeness, appropriateness and quality to drive trusted decisions

3. Explainability & Transparency

Responsible disclosure and design that answers the questions of how and why a conclusion was drawn from an AI Solution, ensuring stakeholders understand what is happening in the AI lifecycle

4. Fairness

AI solutions ensure unbiased decision-making to foster trust and collaboration among brokers, regulators, industry groups, technology providers and member associations

5. Sustainability

AI solutions contribute to the value of ensuring a vibrant and long-term future for the broker distribution channel

6. Accountability & Human Connection

AI solutions ensure human oversight and responsibitly to be answerable to customers to safeguarding their interests, managing risks and ensure compliance with applicable laws and regulations

7. AI-Driven Efficiency & Customers Service

Optimize workflows with AI solutions to improve claims processing and underwritng, while tailoring solutions through predictive analytics. Ensure human oversight is preserved for handling complex cases

AI Governance Guidance

Get practical insights on responsible AI adoption, risk management, and compliance in one concise guide.

Download the AI Governance Guide (PDF)

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Aligning with Regulatory Frameworks and Standards

Principles will align with international AI guidelines (e.g., ISO 42001, OECD frameworks) and industry-specific regulations (e.g., RIBO, OSFI, AMF) upheld by stakeholders. Adherence to legal standards will help organizations navigate jurisdictional requirements, promote sustainable practices, and prevent misuse of AI data

Encouraging Responsible Al Innovation

Principles will encourage the broker community to innovate responsibly by developing AI systems that prioritize consumer well-being, inclusivity, and fairness, while also assessing the societal, environmental, and economic impacts of their AI solutions

Promoting Accountability in AI Oversight

AI principles will reinforce accountability across all levels of organizations and third-party collaborators. By defining roles, ensure human oversight in AI processes, this enhances traceability, enables informed decision-making, and embeds mechanisms for ethical redress when errors or adverse outcomes occur

Ensuring Consumer Trust and Fairness

AI governance principles support commitment to transparency, fairness, and accountability. By requiring explainable outcomes and proactive consumer communication, it fosters trust among the broader broker community, their clients, and external stakeholders

Supporting Ethical Standards and Stakeholder Collaboration

By incorporating AI governance principles, broker members can align with its mission of fostering an ethical culture among its stakeholders Address biases, safeguard consumer protection, and promote inclusivity, which will reinforce commitment to ethical AI practices in collaboration with industry stakeholders, regulators, and third-party solution providers

PoC Use Case Overview: Al-Assisted Coverage Discovery & Gap Analysis

Technical Requirements

AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.

Integration with BMS to retrieve client profiles, exposure information, and historical policy data.

Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.

Data security and compliance features to protect client information.

Functional Scope

Analyze client-submitted data, including exposures, business context, and other relevant information.

Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).

Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.

Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.

Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.

AI-Powered Client Onboarding & Data Intake – Overview:
Proof of Concept (PoC) Use Case

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.