Applied is rallying around a clear ambition: to deliver open innovation across everything we do. And we’re not just talking about it—we are executing. We are delivering practical, innovative solutions that provide our broker and insurer partners choice and flexibility, driving greater automation, intelligence, and insight so the brokerage channel can compete and grow.

For the Canadian market, we are strengthening this commitment by standardizing best‑practice workflows across the broker lifecycle and aligning them with our Customer Success and Service organization. This ensures Canadian brokers benefit from consistent, optimized processes that improve efficiency, drive adoption, and support sustainable, long‑term growth.

We are evolving Applied Epic into an AI‑amplified, browser‑based system of action that connects the full Digital Roundtrip of Insurance—sales, servicing, submissions/quoting, and finance. Our strategy and roadmap build on years of investment, embedding data and AI directly into the workflows that matter most. The result: our partners can work faster, smarter, and more profitably, supported by best practices and a customer success model designed for the Canadian broker channel to thrive.

Applied Systems is the leading global provider of cloud-based software that powers the business of insurance. Recognized as a pioneer in insurance automation and the innovation leader, Applied is the world’s largest provider of brokerage management systems. By automating the insurance lifecycle, Applied’s people and products enable millions of people around the world to safeguard and protect what matters most.



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Services / Assets

The Digital Growth Era
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Your Co-Pilot to a Higer Value Insurance Brokerage
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Empowering the Rising Generations in Insurance
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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.