Accueil » Stackathon Segments » Policy Management

The following vendor(s) were chosen

by the Stackathon broker participants

as their top choice(s) in Policy Management

Applied

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Trufla truMarket

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Power Broker

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Policy Management Scoring Criteria


Client and Policy Management (5 points)
Description: The ability to manage clients and their policies, including tracking renewals, endorsements, and customer interactions.
Considerations: Does the BMS provide an easy way to access and manage client profiles, policy histories, and policy changes?


Integration with Other Systems (5 points)
Description: How well the BMS integrates with other key systems like quoting/rating tools, carrier platforms, CRM systems, and accounting software.
Considerations: Does the system offer seamless integration with external systems like carriers and quoting platforms? Is there a strong
API for easy integration with other tools?


Automation of Routine Tasks (5 points)
Description: The ability to automate recurring tasks like policy renewals, invoicing, and generating reports.
Considerations: How well does the system automate key tasks? Can it automatically trigger renewals, reminders, and other routine workflows?


Pricing and Value for Money (5 points)
Description: The pricing model and the overall value offered by the BMS in relation to its features and functionality.
Considerations: Is the pricing transparent and reasonable based on the features offered? Does it provide good value for money in terms of scalability and
usability?


Reporting and Analytics (5 points)
Description: The system’s ability to generate comprehensive reports on policy insights, book trends, KPIs, and provide granular information on coverage,
policy details, and overall business performance.
Considerations: Does the system offer detailed reports with policy insights? Can it break down data into granular coverage details, trends in the book of
business, and track key performance indicators (KPIs)?

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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.