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AI-Powered Client Onboarding & Data Intake

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

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AI Use Case Description

Deploy an AI-powered personal lines onboarding agent to streamline client intake. The agent will collect client information, answer onboarding questions, and guide users through the process. It will also process renewal documents from selected carriers, extract key data, and integrate with the BMS to enable faster, more accurate quoting and reduce manual effort.

Requirements & Scope

Technical Requirements

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Functional Scope

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Pilot Details

Pilot Parameters

Duration: 3 months

End Users: Brokers within the participating brokerage

Policy Types in Scope: Commercial policies of sufficient size; currently selling specialty products

Data Sources: Client submissions, current policy documents, industry standards information

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

Potential Pilot Costs

Licensing Costs: Licensing cost of the vendor solution

Integration Costs: API configuration with BMS and ARS

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

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

Dependencies

Broker IT support and environment readiness:

Required to deploy and integrate within the broker’s system

Access to accurate and up-to-date policy documents

Availability of typical coverage patterns and industry guidance

Engaged users and timely feedback loops

Other Considerations

Scalability Within Brokerage: Can be scaled across different teams and policy types within an individual brokerage

Scalability Across Brokerages: Can be deployed and scaled across other brokerages

Change Management & Training: Brokers may need targeted training sessions and ongoing support to ensure adoption

Values & Benefits

Reduces manual data entry and improves intake consistency.

Lowers risk of onboarding errors that affect downstream processes.
Enhances client onboarding and self-service options with a faster, guided experience.
Sell opportunities profiling and cross-analytics; improves internal data quality and reduces risk.
Reduces back-and-forth communication during intake.
Frees up broker capacity, allowing more time for high-value client engagement.

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-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.