Accueil » AI Project

Technology is moving fast — and IBAC
wants brokers in the driver’s seat.

Empowering Brokers Through AI Education

That’s why we created the IBAC AI Working Group: to help Canadian insurance brokers better understand and use artificial intelligence in a way that makes sense for their business.


To get started, we partnered with KPMG on a national initiative that brings practical, broker-focused AI education to the forefront.

The first phase of this project focused on building a strong foundation. Together, we developed three educational sessions to help demystify AI — breaking down where it fits, why it matters, and how brokers can approach it responsibly.
 


But this isn’t just theory. We identified 10 real-world AI use cases tailored to brokers — from improving client intake to streamlining operations. From those, we pulled the two most promising examples and built simple one-pagers to help brokers see what’s possible.
 


We also tackled one of the biggest questions facing any new tech: governance. How do brokers adopt AI while staying aligned with regulations, ethical standards, and evolving industry expectations? To answer that, we reviewed the latest guidance from RIBO, AMF, and OSFI, and developed practical AI principles to help brokers move forward confidently — and responsibly.

AI Education Sessions
AI PoC Use Cases
AI Governance Guidance

Our Sponsors

Become a Part of the Community.

Subscribe today and Stay Updated! Get the latest updates, news, and exclusive content straight to your inbox.

By submitting this form, you are consenting to receive marketing emails from: My WordPress. You can revoke your consent to receive emails at any time by using the SafeUnsubscribe® link, found at the bottom of every email. Emails are serviced by Constant Contact

© 2025, Insurance Brokers Association of Canada

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.