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AI - Assisted Coverage Discovery & Gap Analysis

Proof of Concept (PoC) Use Case · IBAC AI Working Group

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AI Use Case Description

Use AI to analyze client data, industry benchmarks, and existing policies to recommend relevant coverage options and flag potential shortfalls. In areas like cyber, AI can interpret exposures, peer benchmarks, and typical industry requirements to support smarter, faster discovery conversations and enable more tailored solution development

Requirements & Scope

Technical Requirements

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Functional Scope

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Pilot Details

Pilot Parameters

Duration: 3 months

End Users: New clients and brokers within the participating brokerage

Business Lines in Scope: Personal lines (home and automobile)

Data Scope: Renewal documents from 5-6 carriers selected by the broker

Hosting & Deployment: Supported by the vendor in a secure environment and integrated into the broker’s systems

Metrics / KPIs: Efficiency improvements, customer satisfaction enhancements, etc.

Potential Pilot Costs

Licensing Costs: Licensing cost of the vendor solution

Integration Costs: API configuration with BMS and ARS

Data Costs: Access to historical onboarding data for training/validation

Hypercare / Ongoing Support: Vendor training, implementation support, and ongoing maintenance during the pilot

Dependencies

Accuracy and completeness of onboarding templates and internal rules

Broker system readiness for chatbot integration and validation logic

Availability of onboarding records for pilot calibration

Client consent for use of AI in onboarding process

Other Considerations

Workflow Fit: Onboarding workflows, data validation rules, and exception categories should be mapped to align with the broker’s existing intake processes

Client Experience: Tone, accessibility, and usability of the chatbot should algin with client expectations and brand standards to ensure adoption

Fallback Plan: Manual intake processes should remain available if the chatbot fails / clients opts out

Future Scalability: For broader adoption post-PoC, a dynamic webform can be introduced to deliver a more professional and polished client interface

Values & Benefits

Support brokers with market insights by benchmarking against peer data and industry standards
Speeds up discovery conversations by highlighting key gaps upfront, reducing prep time for brokers
Creates a scalable foundation for broader AI adoption across policy types and lines of business
Mitigates underinsurance risk and drives revenue through cross-sell and up-sell insights
Enhances quality of coverage recommendations through data-driven analysis
Facilitates more tailored and information conversations with clients

Current State & Future State Broker Workflow

Key Enhancements with AI

  • A structured, guided chatbot interaction with dynamic prompts adapts based on prior inputs to simplify data intake and ensure completeness.
  • Minimizes manual parsing and transcription time while enabling quick extraction of relevant information.
  • AI detects inconsistencies, missing fields, or abnormal values, reducing back-and-forth communication and ensuring cleaner, verified data early in the process.
  • Final validated data is auto-fed into internal systems Generate accurate, timely quotes across selected carriers
  • All data and interactions are logged centrally for traceability

Downloads & Supporting Resources

Discover practical AI proof-of-concept examples, key metrics, and lessons learned to guide your AI initiatives.

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View PoC Case Study (PDF)

AI - 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

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.

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.